 

 
 
 
 
 
 
 
 
 
 
 
 

AI Now Report 2018 
Meredith Whittaker, AI Now Institute, New York University, Google Open Research  
Kate Crawford, AI Now Institute, New York University, Microsoft Research 
Roel Dobbe, AI Now Institute, New York University 
Genevieve Fried, AI Now Institute, New York University 
Elizabeth Kaziunas, AI Now Institute, New York University 
Varoon Mathur, AI Now Institute, New York University 
Sarah Myers West, AI Now Institute, New York University 
Rashida Richardson, AI Now Institute, New York University 
Jason Schultz, AI Now Institute, New York University School of Law 
Oscar Schwartz, AI Now Institute, New York University 
 
With research assistance from Alex Campolo and Gretchen Krueger (AI Now Institute, New York 
University) 
 
DECEMBER 2018 
 

 
 

 

 
 

 CONTENTS 
 
ABOUT THE AI NOW INSTITUTE

3 

RECOMMENDATIONS

4 

EXECUTIVE SUMMARY

7 

INTRODUCTION

10 

1. THE INTENSIFYING PROBLEM SPACE

12 

1.1 AI is Amplifying Widespread Surveillance

12 

The faulty science and dangerous history of affect recognition

13 

Facial recognition ampliﬁes civil rights concerns

15 

1.2 The Risks of Automated Decision Systems in Government

18 

1.3 Experimenting on Society: Who Bears the Burden?

22 

2. EMERGING SOLUTIONS IN 2018

24 

2.1 Bias Busting and Formulas for Fairness: the Limits of Technological “Fixes”
Broader approaches

24 
27 

2.2 Industry Applications: Toolkits and System Tweaks

28 

2.3 Why Ethics is Not Enough

29 

3. WHAT IS NEEDED NEXT

32 

3.1 From Fairness to Justice

32 

3.2 Infrastructural Thinking

33 

3.3 Accounting for Hidden Labor in AI Systems

34 

3.4 Deeper Interdisciplinarity

36 

3.5 Race, Gender and Power in AI

37 

3.6 Strategic Litigation and Policy Interventions

39 

3.7 Research and Organizing: An Emergent Coalition

40 

CONCLUSION

42 

ENDNOTES

44 

 
 
 
 
 
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License 

 

2 

 ABOUT THE AI NOW INSTITUTE  
 

The AI Now Institute at New York University is an interdisciplinary research institute dedicated to 
understanding the social implications of AI technologies. It is the ﬁrst university research center 
focused speciﬁcally on AI’s social signiﬁcance. Founded and led by Kate Crawford and Meredith 
Whittaker, AI Now is one of the few women-led AI institutes in the world.  
 
AI Now works with a broad coalition of stakeholders, including academic researchers, industry, 
civil society, policy makers, and affected communities, to identify and address issues raised by 
the rapid introduction of AI across core social domains. AI Now produces interdisciplinary 
research to help ensure that AI systems are accountable to the communities and contexts they 
are meant to serve, and that they are applied in ways that promote justice and equity. The 
Institute’s current research agenda focuses on four core areas: bias and inclusion, rights and 
liberties, labor and automation, and safety and critical infrastructure.  
 
Our most recent publications include: 
● Litigating Algorithms, a major report assessing recent court cases focused on 
government use of algorithms  
● Anatomy of an AI System, a large-scale map and longform essay produced in partnership 
with SHARE Lab, which investigates the human labor, data, and planetary resources 
required to operate an Amazon Echo 
● Algorithmic Impact Assessment (AIA) Report, which helps affected communities and 
stakeholders assess the use of AI and algorithmic decision-making in public agencies  
● Algorithmic Accountability Policy Toolkit, which is geared toward advocates interested 
in understanding government use of algorithmic systems 
 
We also host expert workshops and public events on a wide range of topics. Our workshop on 
Immigration, Data, and Automation in the Trump Era, co-hosted with the Brennan Center for 
Justice and the Center for Privacy and Technology at Georgetown Law, focused on the Trump 
Administration’s use of data harvesting, predictive analytics, and machine learning to target 
immigrant communities. The Data Genesis Working Group convenes experts from across 
industry and academia to examine the mechanics of dataset provenance and maintenance. Our 
roundtable on Machine Learning, Inequality and Bias, co-hosted in Berlin with the Robert Bosch 
Academy, gathered researchers and policymakers from across Europe to address issues of bias, 
discrimination, and fairness in machine learning and related technologies. 
 
Our annual public symposium convenes leaders from academia, industry, government, and civil 
society to examine the biggest challenges we face as AI moves into our everyday lives. The AI 
Now 2018 Symposium addressed the intersection of AI ethics, organizing, and accountability, 
examining the landmark events of the past year. Over 1,000 people registered for the event, which 
was free and open to the public. Recordings of the program are available on our website. 
More information is available at www.ainowinstitute.org. 
 
3 

 RECOMMENDATIONS 
 
1. Governments need to regulate AI by expanding the powers of sector-speciﬁc agencies to 
oversee, audit, and monitor these technologies by domain. The implementation of AI 
systems is expanding rapidly, without adequate governance, oversight, or accountability 
regimes. Domains like health, education, criminal justice, and welfare all have their own 
histories, regulatory frameworks, and hazards. However, a national AI safety body or general 
AI standards and certiﬁcation model will struggle to meet the sectoral expertise requirements 
needed for nuanced regulation. We need a sector-speciﬁc approach that does not prioritize 
the technology, but focuses on its application within a given domain. Useful examples of 
sector-speciﬁc approaches include the United States Federal Aviation Administration and the 
National Highway Traﬃc Safety Administration. 
 
2. Facial recognition and affect recognition need stringent regulation to protect the public 
interest. Such regulation should include national laws that require strong oversight, clear 
limitations, and public transparency. Communities should have the right to reject the 
application of these technologies in both public and private contexts. Mere public notice of 
their use is not suﬃcient, and there should be a high threshold for any consent, given the 
dangers of oppressive and continual mass surveillance. Affect recognition deserves particular 
attention. Affect recognition is a subclass of facial recognition that claims to detect things 
such as personality, inner feelings, mental health, and “worker engagement” based on images 
or video of faces. These claims are not backed by robust scientiﬁc evidence, and are being 
applied in unethical and irresponsible ways that often recall the pseudosciences of phrenology 
and physiognomy. Linking affect recognition to hiring, access to insurance, education, and 
policing creates deeply concerning risks, at both an individual and societal level.   
 
3. The AI industry urgently needs new approaches to governance. As this report 
demonstrates, internal governance structures at most technology companies are failing to 
ensure accountability for AI systems. Government regulation is an important component, 
but leading companies in the AI industry also need internal accountability structures that go 
beyond ethics guidelines. This should include rank-and-ﬁle employee representation on the 
board of directors, external ethics advisory boards, and the implementation of independent 
monitoring and transparency efforts. Third party experts should be able to audit and publish 
about key systems, and companies need to ensure that their AI infrastructures can be 
understood from “nose to tail,” including their ultimate application and use. 
 
4. AI companies should waive trade secrecy and other legal claims that stand in the way of 
accountability in the public sector. Vendors and developers who create AI and automated 
decision systems for use in government should agree to waive any trade secrecy or other 
legal claim that inhibits full auditing and understanding of their software. Corporate secrecy  
 
 
4 

 laws are a barrier to due process: they contribute to the “black box effect” rendering systems 
opaque and unaccountable, making it hard to assess bias, contest decisions, or remedy 
errors. Anyone procuring these technologies for use in the public sector should demand that 
vendors waive these claims before entering into any agreements. 
 
5. Technology companies should provide protections for conscientious objectors, employee 
organizing, and ethical whistleblowers. Organizing and resistance by technology workers 
has emerged as a force for accountability and ethical decision making. Technology 
companies need to protect workers’ ability to organize, whistleblow, and make ethical choices 
about what projects they work on. This should include clear policies accommodating and 
protecting conscientious objectors, ensuring workers the right to know what they are working 
on, and the ability to abstain from such work without retaliation or retribution. Workers raising 
ethical concerns must also be protected, as should whistleblowing in the public interest. 
 
6. Consumer protection agencies should apply “truth-in-advertising” laws to AI products and 
services. The hype around AI is only growing, leading to widening gaps between marketing 
promises and actual product performance. With these gaps come increasing risks to both 
individuals and commercial customers, often with grave consequences. Much like other 
products and services that have the potential to seriously impact or exploit populations, AI 
vendors should be held to high standards for what they can promise, especially when the 
scientiﬁc evidence to back these promises is inadequate and the longer-term consequences 
are unknown. 
 
7. Technology companies must go beyond the “pipeline model” and commit to addressing the 
practices of exclusion and discrimination in their workplaces. Technology companies and 
the AI ﬁeld as a whole have focused on the “pipeline model,” looking to train and hire more 
diverse employees. While this is important, it overlooks what happens once people are hired 
into workplaces that exclude, harass, or systemically undervalue people on the basis of 
gender, race, sexuality, or disability. Companies need to examine the deeper issues in their 
workplaces, and the relationship between exclusionary cultures and the products they build, 
which can produce tools that perpetuate bias and discrimination. This change in focus needs 
to be accompanied by practical action, including a commitment to end pay and opportunity 
inequity, along with transparency measures about hiring and retention.  
 
8. Fairness, accountability, and transparency in AI require a detailed account of the “full stack 
supply chain.” For meaningful accountability, we need to better understand and track the 
component parts of an AI system and the full supply chain on which it relies: that means 
accounting for the origins and use of training data, test data, models, application program 
interfaces (APIs), and other infrastructural components over a product life cycle. We call this 
accounting for the “full stack supply chain” of AI systems, and it is a necessary condition for a  
 
 
 
5 

  
more responsible form of auditing. The full stack supply chain also includes understanding 
the true environmental and labor costs of AI systems. This incorporates energy use, the use of 
labor in the developing world for content moderation and training data creation, and the 
reliance on clickworkers to develop and maintain AI systems.  
  
9. More funding and support are needed for litigation, labor organizing, and community 
participation on AI accountability issues. The people most at risk of harm from AI systems 
are often those least able to contest the outcomes. We need increased support for robust 
mechanisms of legal redress and civic participation. This includes supporting public 
advocates who represent those cut off from social services due to algorithmic decision 
making, civil society organizations and labor organizers that support groups that are at risk of 
job loss and exploitation, and community-based infrastructures that enable public 
participation.  
 
10. University AI programs should expand beyond computer science and engineering 
disciplines. AI began as an interdisciplinary ﬁeld, but over the decades has narrowed to 
become a technical discipline. With the increasing application of AI systems to social 
domains, it needs to expand its disciplinary orientation. That means centering forms of 
expertise from the social and humanistic disciplines. AI efforts that genuinely wish to address 
social implications cannot stay solely within computer science and engineering departments, 
where faculty and students are not trained to research the social world. Expanding the 
disciplinary orientation of AI research will ensure deeper attention to social contexts, and 
more focus on potential hazards when these systems are applied to human populations.  

 

 

6 

 EXECUTIVE SUMMARY 
 
At the core of the cascading scandals around AI in 2018 are questions of accountability: who is 
responsible when AI systems harm us? How do we understand these harms, and how do we 
remedy them? Where are the points of intervention, and what additional research and regulation is 
needed to ensure those interventions are effective? Currently there are few answers to these 
questions, and the frameworks presently governing AI are not capable of ensuring accountability. 
As the pervasiveness, complexity, and scale of these systems grow, the lack of meaningful 
accountability and oversight – including basic safeguards of responsibility, liability, and due 
process – is an increasingly urgent concern. 
 
Building on our 2016 and 2017 reports, the AI Now 2018 Report contends with this central 
problem and addresses the following key issues: 
 
1. The growing accountability gap in AI, which favors those who create and deploy these 
technologies at the expense of those most affected 
2. The use of AI to maximize and amplify surveillance, especially in conjunction with facial 
and affect recognition, increasing the potential for centralized control and oppression 
3. Increasing government use of automated decision systems that directly impact 
individuals and communities without established accountability structures 
4. Unregulated and unmonitored forms of AI experimentation on human populations  
5. The limits of technological solutions to problems of fairness, bias, and discrimination  
 
Within each topic, we identify emerging challenges and new research, and provide 
recommendations regarding AI development, deployment, and regulation. We offer practical 
pathways informed by research so that policymakers, the public, and technologists can better 
understand and mitigate risks. Given that the AI Now Institute’s location and regional expertise is 
concentrated in the U.S., this report will focus primarily on the U.S. context, which is also where 
several of the world’s largest AI companies are based.  
 
The AI accountability gap is growing: The technology scandals of 2018 have shown that the gap 
between those who develop and proﬁt from AI—and those most likely to suffer the consequences 
of its negative effects—is growing larger, not smaller. There are several reasons for this, including 
a lack of government regulation, a highly concentrated AI sector, insuﬃcient governance 
structures within technology companies, power asymmetries between companies and the people 
they serve, and a stark cultural divide between the engineering cohort responsible for technical 
research, and the vastly diverse populations where AI systems are deployed. These gaps are 
producing growing concern about bias, discrimination, due process, liability, and overall 
responsibility for harm. This report emphasizes the urgent need for stronger, sector-speciﬁc 
research and regulation.  
 

7 

 AI is amplifying widespread surveillance: The role of AI in widespread surveillance has expanded 
immensely in the U.S., China, and many other countries worldwide. This is seen in the growing use 
of sensor networks, social media tracking, facial recognition, and affect recognition. These 
expansions not only threaten individual privacy, but accelerate the automation of surveillance, and 
thus its reach and pervasiveness. This presents new dangers, and magniﬁes many longstanding 
concerns. The use of affect recognition, based on debunked pseudoscience, is also on the rise. 
Affect recognition attempts to read inner emotions by a close analysis of the face and is 
connected to spurious claims about people’s mood, mental health, level of engagement, and guilt 
or innocence. This technology is already being used for discriminatory and unethical purposes, 
often without people’s knowledge. Facial recognition technology poses its own dangers, 
reinforcing skewed and potentially discriminatory practices, from criminal justice to education to 
employment, and presents risks to human rights and civil liberties in multiple countries. 
 
Governments are rapidly expanding the use of automated decision systems without adequate 
protections for civil rights: Around the world, government agencies are procuring and deploying 
automated decision systems (ADS) under the banners of eﬃciency and cost-savings. Yet many of 
these systems are untested and poorly designed for their tasks, resulting in illegal and often 
unconstitutional violations of individual rights. Worse, when they make errors and bad decisions, 
the ability to question, contest, and remedy these is often diﬃcult or impossible. Some agencies 
are attempting to provide mechanisms for transparency, due process, and other basic rights, but 
trade secrecy and similar laws threaten to prevent auditing and adequate testing of these 
systems. Drawing from proactive agency efforts, and from recent strategic litigation, we outline 
pathways for ADS accountability. 
 
Rampant testing of AI systems “in the wild” on human populations: Silicon Valley is known for 
its “move fast and break things” mentality, whereby companies are pushed to experiment with 
new technologies quickly and without much regard for the impact of failures, including who bears 
the risk. In the past year, we have seen a growing number of experiments deploying AI systems “in 
the wild” without proper protocols for notice, consent, or accountability. Such experiments 
continue, due in part to a lack of consequences for failure. When harms occur, it is often unclear 
where or with whom the responsibility lies. Researching and assigning appropriate responsibility 
and liability remains an urgent priority. 
 
The limits of technological ﬁxes to problems of fairness, bias, and discrimination: Much new 
work has been done designing mathematical models for what should be considered “fair” when 
machines calculate outcomes, aimed at avoiding discrimination. Yet, without a framework that 
accounts for social and political contexts and histories, these mathematical formulas for fairness 
will almost inevitably miss key factors, and can serve to paper over deeper problems in ways that 
ultimately increase harm or ignore justice. Broadening perspectives and expanding research into 
AI fairness and bias beyond the merely mathematical is critical to ensuring we are capable of 
addressing the core issues and moving the focus from parity to justice. 
 
8 

 The move to ethical principles: This year saw the emergence of numerous ethical principles and 
guidelines for the creation and deployment of AI technologies, many in response to growing 
concerns about AI’s social implications. But as studies show, these types of ethical commitments 
have little measurable effect on software development practices if they are not directly tied to 
structures of accountability and workplace practices. Further, these codes and guidelines are 
rarely backed by enforcement, oversight, or consequences for deviation. Ethical codes can only 
help close the AI accountability gap if they are truly built into the processes of AI development and 
are backed by enforceable mechanisms of responsibility that are accountable to the public 
interest. 
 
The following report develops these themes in detail, reﬂecting on the latest academic research, 
and outlines seven strategies for moving forward: 
  
1. Expanding AI fairness research beyond a focus on mathematical parity and statistical 
fairness toward issues of justice 
2. Studying and tracking the full stack of infrastructure needed to create AI, including 
accounting for material supply chains 
3. Accounting for the many forms of labor required to create and maintain AI systems 
4. Committing to deeper interdisciplinarity in AI  
5. Analyzing race, gender, and power in AI 
6. Developing new policy interventions and strategic litigation 
7. Building coalitions between researchers, civil society, and organizers within the technology 
sector  
 
These approaches are designed to positively recast the AI ﬁeld and address the growing power 
imbalance that currently favors those who develop and proﬁt from AI systems at the expense of 
the populations most likely to be harmed.  

 

 

9 

 INTRODUCTION 
 

The Social Challenges of AI in 2018 
 
The past year has seen accelerated integration of powerful artiﬁcial intelligence systems into core 
social institutions, against a backdrop of rising inequality, political populism, and industry 
scandals.1 There have been major movements from both inside and outside technology 
companies pushing for greater accountability and justice. The AI Now 2018 Report focuses on 
these themes and examines the gaps between AI ethics and meaningful accountability, and the 
role of organizing and regulation.  
 
In short, it has been a dramatic year in AI. In any normal year, Cambridge Analytica seeking to 
manipulate national elections in the US and UK using social media data and algorithmic ad 
targeting would have been the biggest story.2 But in 2018, it was just one of many scandals. 
Facebook had a series of disasters, including a massive data breach in September,3 multiple class 
action lawsuits for discrimination,4 accusations of inciting ethnic cleansing in Myanmar,5 potential 
violations of the Fair Housing Act,6 and hosting masses of fake Russian accounts.7 Throughout 
the year, the company’s executives were frequently summoned to testify, with Mark Zuckerberg 
facing the US Senate in April and the European Parliament in May.8 Zuckerberg mentioned AI 
technologies over 30 times in his Congressional testimony as the cure-all to the company’s 
problems, particularly in the complex areas of censorship, fairness, and content moderation.9 
 
But Facebook wasn’t the only one in crisis. News broke in March that Google was building AI 
systems for the Department of Defense’s drone surveillance program, Project Maven.10 The news 
kicked off an unprecedented wave of technology worker organizing and dissent across the 
industry.11 In June, when the Trump administration introduced the family separation policy that 
forcibly removed immigrant children from their parents, employees from Amazon, Salesforce, and 
Microsoft all asked their companies to end contracts with U.S. Immigration and Customs 
Enforcement (ICE).12 Less than a month later, it was revealed that ICE modiﬁed its own risk 
assessment algorithm so that it could only produce one result: the system recommended “detain” 
for 100% of immigrants in custody.13 
 
Throughout the year, AI systems continued to be tested on live populations in high-stakes 
domains, with some serious consequences. In March, autonomous cars killed drivers and 
pedestrians.14 Then in May, a voice recognition system in the UK designed to detect immigration 
fraud ended up cancelling thousands of visas and deporting people in error.15 Documents leaked 
in July showed that IBM Watson was producing “unsafe and incorrect” cancer treatment 
recommendations.16 And an investigation in September revealed that IBM was also working with 
the New York City Police Department (NYPD) to build an “ethnicity detection” feature to search 
faces based on race, using police camera footage of thousands of people in the streets of New 
York taken without their knowledge or permission.17  
10 

 This is just a sampling of an extraordinary series of incidents from 2018.18 The response has 
included a growing wave of criticism, with demands for greater accountability from the 
technology industry and the systems they build.19 In turn, some companies have made public 
calls for the U.S. to regulate technologies like facial recognition.20 Others have published AI ethics 
principles and increased efforts to produce technical ﬁxes for issues of bias and discrimination in 
AI systems. But many of these ethical and technical approaches deﬁne the problem space very 
narrowly, neither contending with the historical or social context nor providing mechanisms for 
public accountability, oversight, and due process. This makes it nearly impossible for the public to 
validate that any of the current problems have, in fact, been addressed. 
 
As numerous scholars have noted, one signiﬁcant barrier to accountability is the culture of 
industrial and legal secrecy that dominates AI development.21 Just as many AI technologies are 
“black boxes”, so are the industrial cultures that create them.22 Many of the fundamental building 
blocks required to understand AI systems and to ensure certain forms of accountability – from 
training data, to data models, to the code dictating algorithmic functions, to implementation 
guidelines and software, to the business decisions that directed design and development – are 
rarely accessible to review, hidden by corporate secrecy laws.   
 
The current accountability gap is also caused by the incentives driving the rapid pace of technical 
AI research. The push to “innovate,” publish ﬁrst, and present a novel addition to the technical 
domain has created an accelerated cadence in the ﬁeld of AI, and in technical disciplines more 
broadly. This comes at the cost of considering empirical questions of context and use, or 
substantively engaging with ethical concerns.23 Similarly, technology companies are driven by 
pressures to “launch and iterate,” which assume complex social and political questions will be 
handled by policy and legal departments, leaving developers and sales departments free from the 
responsibility of considering the potential downsides. The “move fast and break things” culture 
provides little incentive for ensuring meaningful public accountability or engaging the 
communities most likely to experience harm.24 This is particularly problematic as the accelerated 
application of AI systems in sensitive social and political domains presents risks to marginalized 
communities.  
 
The challenge to create better governance and greater accountability for AI poses particular 
problems when such systems are woven into the fabric of government and public institutions. 
The lack of transparency, notice, meaningful engagement, accountability, and oversight creates 
serious structural barriers for due process and redress for unjust and discriminatory decisions. 
 
In this year’s report, we assess many pressing issues facing us as AI tools are deployed further 
into the institutions that govern everyday life. We focus on the biggest industry players, because 
the number of companies able to create AI at scale is very small, while their power and reach is 
global. We evaluate the current range of responses from industry, governments, researchers,  
 
 
11 

 activists, and civil society at large. We suggest a series of substantive approaches and make ten 
speciﬁc recommendations. Finally, we share the latest research and policy strategies that can 
contribute to greater accountability, as well as a richer understanding of AI systems in a wider 
social context. 
 
 

1. THE INTENSIFYING PROBLEM SPACE 
 
In identifying the most pressing social implications of AI this year, we look closely at the role of AI 
in widespread surveillance in multiple countries around the world, and at the implications for 
rights and liberties. In particular, we consider the increasing use of facial recognition, and a 
subclass of facial recognition known as affect recognition, and assess the growing calls for 
regulation. Next, we share our ﬁndings on the government use of automated decision systems, 
and what questions this raises for fairness, transparency, and due process when such systems 
are protected by trade secrecy and other laws that prevent auditing and close examination.25 
Finally, we look at the practices of deploying experimental systems “in the wild,” testing them on 
human populations. We analyze who has the most to gain, and who is at greatest risk of 
experiencing harm. 
 

1.1 AI is Amplifying Widespread Surveillance 
 
This year, we have seen AI amplify large-scale surveillance through techniques that analyze video, 
audio, images, and social media content across entire populations and identify and target 
individuals and groups. While researchers and advocates have long warned about the dangers of 
mass data collection and surveillance,26 AI raises the stakes in three areas: automation, scale of 
analysis, and predictive capacity. Speciﬁcally, AI systems allow automation of surveillance 
capabilities far beyond the limits of human review and hand-coded analytics. Thus, they can serve 
to further centralize these capabilities in the hands of a small number of actors. These systems 
also exponentially scale analysis and tracking across large quantities of data, attempting to make 
connections and inferences that would have been diﬃcult or impossible before their introduction. 
Finally, they provide new predictive capabilities to make determinations about individual character 
and risk proﬁles, raising the possibility of granular population controls.  
 
China has offered several examples of alarming AI-enabled surveillance this year, which we know 
about largely because the government openly acknowledges them. However, it’s important to 
note that many of the same infrastructures already exist in the U.S. and elsewhere, often 
produced and promoted by private companies whose marketing emphasizes beneﬁcial use 
cases. In the U.S. the use of these tools by law enforcement and government is rarely open to 
public scrutiny, as we will review, and there is much we do not know. Such infrastructures and 
capabilities could easily be turned to more surveillant ends in the U.S., without public disclosure 
and oversight, depending on market incentives and political will.  
 
12 

 In China, military and state-sanctioned automated surveillance technology is being deployed to 
monitor large portions of the population, often targeting marginalized groups. Reports include 
installation of facial recognition tools at the Hong Kong-Shenzhen border,27 using ﬂocks of robotic 
dove-like drones in ﬁve provinces across the country,28 and the widely reported social credit 
monitoring system,29 each of which illustrates how AI-enhanced surveillance systems can be 
mobilized as a means of far-reaching social control.30  
 
The most oppressive use of these systems is reportedly occuring in the Xinjiang Autonomous 
Region, described by The Economist as a “police state like no other.”31 Surveillance in this Uighur 
ethnic minority area is pervasive, ranging from physical checkpoints and programs where Uighur 
households are required to “adopt” Han Chinese oﬃcials into their family, to the widespread use of 
surveillance cameras, spyware, Wi-Fi sniffers, and biometric data collection, sometimes by 
stealth. Machine learning tools integrate these streams of data to generate extensive lists of 
suspects for detention in re-education camps, built by the government to discipline the group. 
Estimates of the number of people detained in these camps range from hundreds of thousands to 
nearly one million.32  
 
These infrastructures are not unique to China. Venezuela announced the adoption of a new smart 
card ID known as the “carnet de patria,” which, by integrating government databases linked to 
social programs, could enable the government to monitor citizens’ personal ﬁnances, medical 
history, and voting activity.33 In the United States, we have seen similar efforts. The Pentagon has 
funded research on AI-enabled social media surveillance to help predict large-scale population 
behaviors,34 and the U.S. Immigration and Customs Enforcement (ICE) agency is using an 
Investigative Case Management System developed by Palantir and powered by Amazon Web 
Services in its deportation operations.35 The system integrates public data with information 
purchased from private data brokers to create proﬁles of immigrants in order to aid the agency in 
proﬁling, tracking, and deporting individuals.36 These examples show how AI systems increase 
integration of surveillance technologies into data-driven models of social control and amplify the 
power of such data, magnifying the stakes of misuse and raising urgent and important questions 
as to how basic rights and liberties will be protected. 
 

The faulty science and dangerous history of affect recognition 
 
We are also seeing new risks emerging from unregulated facial recognition systems. These 
systems facilitate the detection and recognition of individual faces in images or video, and can be 
used in combination with other tools to conduct more sophisticated forms of surveillance, such 
as automated lip-reading, offering the ability to observe and interpret speech from a distance.37 
 
Among a host of AI-enabled surveillance and tracking techniques, facial recognition raises 
particular civil liberties concerns. Because facial features are a very personal form of biometric 
identiﬁcation that is extremely diﬃcult to change, it is hard to subvert or “opt out” of its operations.  

13 

 And unlike other tracking tools, facial recognition seeks to use AI for much more than simply 
recognizing faces. Once identiﬁed, a face can be linked with other forms of personal records and 
identiﬁable data, such as credit score, social graph, or criminal record. 
 
Affect recognition, a subset of facial recognition, aims to interpret faces to automatically detect 
inner emotional states or even hidden intentions. This approach promises a type of emotional 
weather forecasting: analyzing hundreds of thousands of images of faces, detecting 
“micro-expressions,” and mapping these expressions to “true feelings.”38 This reactivates a long 
tradition of physiognomy – a pseudoscience that claims facial features can reveal innate aspects 
of our character or personality. Dating from ancient times, scientiﬁc interest in physiognomy grew 
enormously in the nineteenth century, when it became a central method for scientiﬁc forms of 
racism and discrimination.39 Although physiognomy fell out of favor following its association with 
Nazi race science, researchers are worried about a reemergence of physiognomic ideas in affect 
recognition applications.40 The idea that AI systems might be able to tell us what a student, a 
customer, or a criminal suspect is really feeling or what type of person they intrinsically are is 
proving attractive to both corporations and governments, even though the scientiﬁc justiﬁcations 
for such claims are highly questionable, and the history of their discriminatory purposes 
well-documented. 
 
The case of affect detection reveals how machine learning systems can easily be used to 
intensify forms of classiﬁcation and discrimination, even when the basic foundations of these 
theories remain controversial among psychologists. The scientist most closely associated with 
AI-enabled affect detection is the psychologist Paul Ekman, who asserted that emotions can be 
grouped into a small set of basic categories like anger, disgust, fear, happiness, sadness, and 
surprise.41 Studying faces, according to Ekman, produces an objective reading of authentic 
interior states—a direct window to the soul. Underlying his belief was the idea that emotions are 
ﬁxed and universal, identical across individuals, and clearly visible in observable biological 
mechanisms regardless of cultural context. But Ekman’s work has been deeply criticized by 
psychologists, anthropologists, and other researchers who have found his theories do not hold up 
under sustained scrutiny.42 The psychologist Lisa Feldman Barrett and her colleagues have 
argued that an understanding of emotions in terms of these rigid categories and simplistic 
physiological causes is no longer tenable.43 Nonetheless, AI researchers have taken his work as 
fact, and used it as a basis for automating emotion detection.44  
 
Contextual, social, and cultural factors — how, where, and by whom such emotional signiﬁers are 
expressed — play a larger role in emotional expression than was believed by Ekman and his peers. 
In light of this new scientiﬁc understanding of emotion, any simplistic mapping of a facial 
expression onto basic emotional categories through AI is likely to reproduce the errors of an 
outdated scientiﬁc paradigm. It also raises troubling ethical questions about locating the arbiter of 
someone’s “real” character and emotions outside of the individual, and the potential abuse of  
 
 
 
14 

 power that can be justiﬁed based on these faulty claims. Psychiatrist Jamie Metzl documents a 
recent cautionary example: a pattern in the 1960s of diagnosing Black people with schizophrenia 
if they supported the civil rights movement.45 Affect detection combined with large-scale facial 
recognition has the potential to magnify such political abuses of psychological proﬁling.  
 
In the realm of education, some U.S. universities have announced plans to use affect analysis 
software on students.46 The University of St. Thomas, in Minnesota, is using a system based on 
Microsoft’s facial recognition and affect detection tools to observe students in the classroom 
using a webcam. The system predicts the students’ emotional state. An overview of student 
sentiment is viewable by the teacher, who can then shift their teaching in a way that “ensures 
student engagement,” as judged by the system. This raises serious questions on multiple levels: 
what if the system, with a simplistic emotional model, simply cannot grasp more complex states? 
How would a student contest a determination made by the system? What if different students are 
seen as “happy” while others are “angry”—how should the teacher redirect the lesson? What are 
the privacy implications of such a system, particularly given that, in the case of the pilot program, 
there is no evidence that students were informed of its use on them? 
 
Outside of the classroom, we are also seeing personal assistants, like Alexa and Siri, seeking to 
pick up on the emotional undertones of human speech, with companies even going so far as to 
patent methods of marketing based on detecting emotions, as well as mental and physical 
health.47 The AI-enabled emotion measurement company Affectiva now promises it can promote 
safer driving by monitoring “driver and occupant emotions, cognitive states, and reactions to the 
driving experience...from face and voice.”48 Yet there is little evidence that any of these systems 
actually work across different individuals, contexts, and cultures, or have any safeguards put in 
place to mitigate concerns about privacy, bias, or discrimination in their operation. Furthermore, 
as we have seen in the large literature on bias and fairness, classiﬁcations of this nature not only 
have direct impacts on human lives, but also serve as data to train and inﬂuence other AI 
systems. This raises the stakes for any use of affect recognition, further emphasizing why it 
should be critically examined and its use severely restricted. 
 

Facial recognition ampliﬁes civil rights concerns 
 
Concerns are intensifying that facial recognition increases racial discrimination and other biases 
in the criminal justice system. Earlier this year, the American Civil Liberties Union (ACLU) 
disclosed that both the Orlando Police Department and the Washington County Sheriff’s 
department were using Amazon’s Rekognition system, which boasts that it can perform “real-time 
face recognition across tens of millions of faces” and detect “up to 100 faces in challenging 
crowded photos.”49 In Washington County, Amazon speciﬁcally worked with the Sheriff’s 
department to create a mobile app that could scan faces and compare them against a database  
 
 
 
15 

 of at least 300,000 mugshots.50 An Amazon representative recently revealed during a talk that 
they have been considering applications where Orlando’s network of surveillance cameras could 
be used in conjunction with facial recognition technology to ﬁnd a “person of interest” wherever 
they might be in the city.51 
 
In addition to the privacy and mass surveillance concerns commonly raised, the use of facial 
recognition in law enforcement has also intersected with concerns of racial and other biases. 
Researchers at the ACLU and the University of California (U.C.) Berkeley tested Amazon’s 
Rekognition tool by comparing the photos of sitting members in the United States Congress with 
a database containing 25,000 photos of people who had been arrested. The results showed 
signiﬁcant levels of inaccuracy: Amazon’s Rekognition incorrectly identiﬁed 28 members of 
Congress as people from the arrest database. Moreover, the false positives disproportionately 
occurred among non-white members of Congress, with an error rate of nearly 40% compared to 
only 5% for white members.52 Such results echo a string of ﬁndings that have demonstrated that 
facial recognition technology is, on average, better at detecting light-skinned people than 
dark-skinned people, and better at detecting men than women.53  
 
In its response to the ACLU, Amazon acknowledged that “the Rekognition results can be 
signiﬁcantly skewed by using a facial database that is not appropriately representative.”54 Given 
the deep and historical racial biases in the criminal justice system, most law enforcement 
databases are unlikely to be “appropriately representative.”55 Despite these serious ﬂaws, ongoing 
pressure from civil rights groups, and protests from Amazon employees over the potential for 
misuse of these technologies, Amazon Web Services CEO Andrew Jassy recently told employees 
that “we feel really great and really strongly about the value that Amazon Rekognition is providing 
our customers of all sizes and all types of industries in law enforcement and out of law 
enforcement.”56 
 
Nor is Amazon alone in implementing facial recognition technologies in unaccountable ways. 
Investigative journalists recently disclosed that IBM and the New York City Police Department 
(NYPD) partnered to develop such a system that included “ethnicity search” as a custom feature, 
trained on thousands of hours of NYPD surveillance footage.57 Use of facial recognition software 
in the private sector has expanded as well.58 Major retailers and venues have already begun using 
these technologies to detect shoplifters, monitor crowds, and even “scan for unhappy customers,” 
using facial recognition systems instrumented with “affect detection” capabilities.59 
 
These concerns are ampliﬁed by a lack of laws and regulations. There is currently no federal 
legislation that seeks to provide standards, restrictions, requirements, or guidance regarding the 
development or use of facial recognition technology. In fact, most existing federal legislation 
looks to promote the use of facial recognition for surveillance, immigration enforcement, 
employment veriﬁcation, and domestic entry-exit systems.60 The laws that we do have are 
piecemeal, and none speciﬁcally address facial recognition. Among these is the Biometric 
Information Privacy Act, a 2008 Illinois law that sets forth stringent rules regarding the collection 
of biometrics. While the law does not mention facial recognition, given that the technology was 
16 

 not widely available in 2008, many of its requirements, such as obtaining consent, are reasonably 
interpreted to apply.61 More recently, several municipalities and a local transit system have 
adopted ordinances that seek to create greater transparency and oversight of data collection and 
use requirements regarding the acquisition of surveillance technologies, which would include 
facial recognition based on the expansive deﬁnition in these ordinances.62 
 
Opposition to the use of facial recognition tools by government agencies is growing. Earlier this 
year, AI Now joined the ACLU and over 30 other research and advocacy organizations calling on 
Amazon to stop selling facial recognition software to government agencies after the ACLU 
uncovered documents showing law enforcement use of Amazon’s Rekognition API.63 Members of 
Congress are also pushing Amazon to provide more information.64  
 
Some have gone further, calling for an outright ban. Scholars Woodrow Hartzog and Evan Selinger 
argue that facial recognition technology is a “tool for oppression that’s perfectly suited for 
governments to display unprecedented authoritarian control and an all-out privacy-eviscerating 
machine,” necessitating extreme caution and diligence before being applied in our contemporary 
digital ecosystem.65 Critiquing the Stanford “gaydar” study that claimed its deep neural network 
was more accurate than humans at predicting sexuality from facial images,66 Frank Pasquale 
wrote that “there are some scientiﬁc research programs best not pursued - and this might be one 
of them.”67  
 
Kade Crockford, Director of the Technology for Liberty Program at ACLU of Massachusetts, also 
wrote in favor of a ban, stating that “artiﬁcial intelligence technologies like face recognition 
systems fundamentally change the balance of power between the people and the 
government...some technologies are so dangerous to that balance of power that they must be 
rejected.”68 Microsoft President Brad Smith has called for government regulation of facial 
recognition, while Rick Smith, CEO of law enforcement technology company Axon, recently stated 
that the “accuracy thresholds” of facial recognition tools aren’t “where they need to be to be 
making operational decisions.”69 
 
The events of this year have strongly underscored the urgent need for stricter regulation of both 
facial and affect recognition technologies. Such regulations should severely restrict use by both 
the public and the private sector, and ensure that communities affected by these technologies are 
the ﬁnal arbiters of whether they are used at all. This is especially important in situations where 
basic rights and liberties are at risk, requiring stringent oversight, audits, and transparency. 
Linkages should not be permitted between private and government databases. At this point, given 
the evidence in hand, policymakers should not be funding or furthering the deployment of these 
systems in public spaces. 
 

17 

 1.2 The Risks of Automated Decision Systems in 
Government 
 
Over the past year, we have seen a substantial increase in the adoption of Automated Decision 
Systems (ADS) across government domains, including criminal justice, child welfare, education, 
and immigration. Often adopted under the theory that they will improve government eﬃciency or 
cost-savings, ADS seek to aid or replace various decision-making processes and policy 
determinations. However, because the underlying models are often proprietary and the systems 
frequently untested before deployment, many community advocates have raised signiﬁcant 
concerns about lack of due process, accountability, community engagement, and auditing.70 
 
Such was the case for Tammy Dobbs, who moved to Arkansas in 2008 and signed up for a state 
disability program to help her with her cerebral palsy.71 Under the program, the state sent a 
qualiﬁed nurse to assess Tammy to determine the number of caregiver hours she would need. 
Because Tammy spent most of her waking hours in a wheelchair and had stiffness in her hands, 
her initial assessment allocated 56 hours of home care per week. Fast forward to 2016, when the 
state assessor arrived with a new ADS on her laptop. Using a proprietary algorithm, this system 
calculated the number of hours Tammy would be allotted. Without any explanation or opportunity 
for comment, discussion, or reassessment, the program allotted Tammy 32 hours per week, a 
massive and sudden drop that Tammy had no chance to prepare for and that severely reduced 
her quality of life. 
 
Nor was Tammy’s situation exceptional. According to Legal Aid of Arkansas attorney Kevin De 
Liban, hundreds of other individuals with disabilities also received dramatic reductions in hours, all 
without any meaningful opportunity to understand or contest their allocations. Legal Aid 
subsequently sued the State of Arkansas, eventually winning a ruling that the new algorithmic 
allocation program was erroneous and unconstitutional. Yet by then, much of the damage to the 
lives of those affected had been done.72 
 
The Arkansas disability cases provide a concrete example of the substantial risks that occur 
when governments use ADS in decisions that have immediate impacts on vulnerable populations. 
While individual assessors may also suffer from bias or ﬂawed logic, the impact of their 
case-by-case decisions has nowhere near the magnitude or scale that a single ﬂawed ADS can 
have across an entire population. 
 
The increased introduction of such systems comes at a time when, according to the World 
Income Inequality Database, the United States has the highest income inequality rate of all 
western countries.73 Moreover, Federal Reserve data shows wealth inequalities continue to grow, 
and racial wealth disparities have more than tripled in the last 50 years, with current policies set to 
exacerbate such problems.74 In 2018 alone, we have seen a U.S. executive order cutting funding 
for social programs that serve the country’s poorest citizens,75 alongside a proposed federal 
18 

 budget that will signiﬁcantly reduce low-income and affordable housing,76 the implementation of 
onerous work requirements for Medicaid,77 and a proposal to cut food assistance beneﬁts for 
low-income seniors and people with disabilities.78  
 
In the context of such policies, agencies are under immense pressure to cut costs, and many are 
looking to ADS as a means of automating hard decisions that have very real effects on those 
most in need.79 As such, many ADS systems are often implemented with the goal of doing more 
with less in the context of austerity policies and cost-cutting. They are frequently designed and 
conﬁgured primarily to achieve these goals, with their ultimate effectiveness being evaluated 
based on their ability to trim costs, often at the expense of the populations such tools are 
ostensibly intended to serve.80 As researcher Virginia Eubanks argues, “What seems like an effort 
to lower program barriers and remove human bias often has the opposite effect, blocking 
hundreds of thousands of people from receiving the services they deserve.”81 
 
When these problems arise, they are frequently diﬃcult to remedy. Few ADS are designed or 
implemented in ways that easily allow affected individuals to contest, mitigate, or ﬁx adverse or 
incorrect decisions. Additionally, human discretion and the ability to intervene or override a 
system’s determination is often substantially limited or removed from case managers, social 
workers, and others trained to understand the context and nuance of a particular person and 
situation.82 These front-line workers become mere intermediaries, communicating inﬂexible 
decisions made by automated systems, without the ability to alter them. 
 
Unlike the civil servants who have historically been responsible for such decisions, many ADS 
come from private vendors and are frequently implemented without thorough testing, review, or 
auditing to ensure their ﬁtness for a given domain.83 Nor are these systems typically built with any 
explicit form of oversight or accountability. This makes discovery of problematic automated 
outcomes diﬃcult, especially since such errors and evidence of discrimination frequently 
manifest as collective harms, only recognizable as a pattern across many individual cases. 
Detecting such problems requires oversight and monitoring. It also requires access to data that is 
often neither available to advocates and the public nor monitored by government agencies. 
 
For example, the Houston Federation of Teachers sued the Houston Independent School District 
for procuring a third-party ADS to use student test data to make teacher employment decisions, 
including which teachers were promoted and which were terminated. It was revealed that no one 
in the district – not a single employee – could explain or even replicate the determinations made 
by the system, even though the district had access to all the underlying data.84 Teachers who 
sought to contest the determinations were told that the “black box” system was simply to be 
believed and could not be questioned. Even when the teachers brought a lawsuit, claiming 
constitutional, civil rights, and labor law violations, the ADS vendor fought against providing any 
access to how its system worked. As a result, the judge ruled that the use of this ADS in public 
employee cases could run afoul of constitutional due process protections, especially when trade 
secrecy blocked employees’ ability to understand how decisions were made. The case has 
subsequently been settled, with the District agreeing to abandon the third-party ADS. 
19 

  
Similarly, in 2013, Los Angeles County adopted an ADS to assess imminent danger or harm to 
children, and to predict the likelihood of a family being re-referred to the child welfare system 
within 12 to 18 months. The County did not perform a review of the system or assess the eﬃcacy 
of using predictive analytics for child safety and welfare. It was only after the death of a child 
whom the system failed to identify as at-risk that County leadership directed a review, which 
raised serious questions regarding the system’s validity. The review speciﬁcally noted that the 
system failed to provide a comprehensive picture of a given family, “but instead focus[ed] on a few 
broad strokes without giving weight to important nuance.”85 Virginia Eubanks found similar 
problems in her investigation of an ADS developed by the same private vendor for use in 
Allegheny County, PA. This system produced biased outcomes because it signiﬁcantly 
oversampled poor children from working class communities, especially communities of color, in 
effect subjecting poor parents and children to more frequent investigation.86  
 
Even in the face of acknowledged issues of bias and the potential for error in high-stakes 
domains, these systems are being rapidly adopted. The Ministry of Social Development in New 
Zealand supported the use of a predictive ADS system to identify children at risk of maltreatment, 
despite their recognizing that the system raised “signiﬁcant ethical concerns.” They defended this 
on the grounds that the beneﬁts “plausibly outweighed” the potential harms, which included 
reconﬁguring child welfare as a statistical issue.87  
 
These cases not only highlight the need for greater transparency, oversight, and accountability in 
the adoption, development, and implementation of ADS, but also the need for examination of the 
limitations of these systems overall, and of the economic and policy factors that accompany the 
push to apply such systems. Virginia Eubanks, who investigated Allegheny County’s use of an 
ADS in child welfare, looked at this and a number of case studies to show how ADS are often 
adopted to avoid or obfuscate broader structural and systemic problems in society – problems 
that are often beyond the capacity of cash-strapped agencies to address meaningfully.88  
 
Other automated systems have also been proposed as a strategy to combat pre-existing 
problems within government systems. For years, criminal justice advocates and researchers have 
pushed for the elimination of cash bail, which has been shown to disproportionately harm 
individuals based on race and socioeconomic status while at the same time failing to enhance 
public safety.89 In response, New Jersey and California recently passed legislation aimed at 
addressing this concern. However, instead of simply ending cash bail, they replaced it with a 
pretrial assessment system designed to algorithmically generate “risk” scores that claim to 
predict whether a person should go free or be detained in jail while awaiting trial.90 
 
The shift from policies such as cash bail to automated systems and risk assessment scoring is 
still relatively new, and is proceeding even without substantial research examining the potential to 
amplify discrimination within the criminal justice system. Yet there are some early indicators that 
raise concern. New Jersey’s law went into effect in 2017, and while the state has experienced a 
decline in its pretrial population, advocates have expressed worry that racial disparities in the risk 
20 

 assessment system persist.91 Similarly, when California’s legislation passed earlier this year, many 
of the criminal justice advocates who pushed for the end of cash bail, and supported an earlier 
version of the bill, opposed its ﬁnal version due to the risk assessment requirement.92 
 
Education policy is also feeling the impact of automated decision systems. A University College 
London professor is among those who argued for AI to replace standardized testing, suggesting 
that UCL Knowledge Lab’s AIAssess can be “trusted...with the assessment of our children’s 
knowledge and understanding,” and can serve to replace or augment more traditional testing.93 
However, much like other forms of AI, there is a growing body of research that shows automated 
essay scoring systems may encode bias against certain linguistic and ethnic groups in ways that 
replicate patterns of marginalization.94 Unfair decisions based on automated scores assigned to 
students from historically and systemically disadvantaged groups are likely to have profound 
consequences on children’s lives, and to exacerbate existing disparities in access to employment 
opportunities and resources.95  
 
The implications of educational ADS go beyond testing to other areas, such as school 
assignments and even transportation. The City of Boston was in the spotlight this year after two 
failed efforts to address school equity via automated systems. First, the school district adopted a 
geographically-driven school assignment algorithm, intended to provide students access to higher 
quality schools closer to home. The city’s goal was to increase the racial and geographic 
integration in the school district, but a report assessing the impact of the system determined that 
it did the opposite: while it shortened student commutes, it ultimately reduced school 
integration.96 Researchers noted that this was, in part, because it was impossible for the system 
to meet its intended goal given the history and context within which it was being used. The 
geographic distribution of quality schools in Boston was already inequitable, and the pre-existing 
racial disparities that played a role in placement at these schools created complications that 
could not be overcome by an algorithm.97  
 
Following this, the Boston school district tried again to use an algorithmic system to improve 
inequity, this time designing it to reconﬁgure school start times – aiming to begin high school 
later, and middle school earlier. This was done in an effort to improve student health and 
performance based on a recognition of students’ circadian rhythms at different ages, and to 
optimize use of school buses to produce cost savings. It also aimed to increase racial equity, 
since students of color primarily attended schools with inconvenient start times compounded by 
long bus rides. The city developed an ADS that optimized for these goals. However, it was never 
implemented because of signiﬁcant public backlash, which ultimately resulted in the resignation 
of the superintendent.98 
 
In this case, the design process failed to adequately recognize the needs of families, or include 
them in deﬁning and reviewing system goals. Under the proposed system, parents with children in 
both high school and middle school would need to reconﬁgure their schedules for vastly different 
start and end times, putting strain on those without this ﬂexibility. The National Association for 
the Advancement of Colored People (NAACP) and the Lawyers’ Committee for Civil Rights and 
21 

 Economic Justice opposed the plan because of the school district’s failure to appreciate that 
parents of color and lower-income parents often rely on jobs that lack work schedule ﬂexibility 
and may not be able to afford additional child care.99 
 
These failed efforts demonstrate two important issues that policymakers must consider when 
evaluating the use of these systems. First, unaddressed structural and systemic problems will 
persist and will likely undermine the potential beneﬁts of these systems if they are not addressed 
prior to a system’s design and implementation. Second, robust and meaningful community 
engagement is essential before a system is put in place and should be included in the process of 
establishing a system’s goals and purpose.  
 
In AI Now’s Algorithmic Impact Assessment (AIA) framework, community engagement is an 
integral part of any ADS accountability process, both as part of the design stage as well as before, 
during, and after implementation.100 When affected communities have the opportunity to assess 
and potentially reject the use of systems that are not acceptable, and to call out fundamental 
ﬂaws in the system before it is put in place, the validity and legitimacy of the system is vastly 
improved. Such engagement serves communities and government agencies: if parents of color 
and lower-income parents in Boston were meaningfully engaged in assessing the goals of the 
school start time algorithmic intervention, their concerns might have been accounted for in the 
design of the system, saving the city time and resources, and providing a much-needed model of 
oversight.  
  
Above all, accountability in the government use of algorithmic systems is impossible when the 
systems making recommendations are “black boxes.” When third-party vendors insist on trade 
secrecy to keep their systems opaque, it makes any path to redress or appeal extremely 
diﬃcult.101 This is why vendors should waive trade secrecy and other legal claims that would 
inhibit the ability to understand, audit, or test their systems for bias, error, or other issues. It is 
important for both people in government and those who study the effects of these systems to 
understand why automated recommendations are made, and to be able to trust their validity. It is 
even more critical that those whose lives are negatively impacted by these systems be able to 
contest and appeal adverse decisions.102 
 
Governments should be cautious: while automated decision systems may promise short-term 
cost savings and eﬃciencies, it is governments, not third party vendors, who will ultimately be 
held responsible for their failings. Without adequate transparency, accountability, and oversight, 
these systems risk introducing and reinforcing unfair and arbitrary practices in critical 
government determinations and policies.103 
 

1.3 Experimenting on Society: Who Bears the Burden? 
 
Over the last ten years, the funding and focus on technical AI research and development has 
accelerated. But efforts at ensuring that these systems are safe and non-discriminatory have not 
22 

 received the same resources or attention. Currently, there are few established methods for 
measuring, validating, and monitoring the effects of AI systems “in the wild”. AI systems tasked 
with signiﬁcant decision making are effectively tested on live populations, often with little 
oversight or a clear regulatory framework.  
 
For example, in March 2018, a self-driving Uber was navigating the Phoenix suburbs and failed to 
“see” a woman, hitting and killing her.104 Last March, Tesla conﬁrmed that a second driver had 
been killed in an accident in which the car’s autopilot technology was engaged.105 Neither 
company suffered serious consequences, and in the case of Uber, the person minding the 
autonomous vehicle was ultimately blamed, even though Uber had explicitly disabled the vehicle’s 
system for automatically applying brakes in dangerous situations.106 Despite these fatal errors, 
Alphabet Inc.’s Waymo recently announced plans for an “early rider program” in Phoenix.107 
Residents can sign up to be Waymo test subjects, and be driven automatically in the process.  
 
Many claim that the occasional autonomous vehicle fatality needs to be put in the context of the 
existing ecosystem, in which many driving-related deaths happen without AI.108 However, because 
regulations and liability regimes govern humans and machines differently, risks generated from 
machine-human interactions do not cleanly fall into a discrete regulatory or accountability 
category. Strong incentives for regulatory and jurisdictional arbitrage exist in this and many other 
AI domains. For example, the fact that Phoenix serves as the site of Waymo and Uber testing is 
not an accident. Early this year, Arizona, perhaps swayed by a promise of technology jobs and 
capital, made oﬃcial what the state allowed in practice since 2015: fully autonomous vehicles 
without anyone behind the wheel are permitted on public roads. This policy was put in place 
without any of the regulatory scaffolding that would be required to contend with the complex 
issues that are raised in terms of liability and accountability. In the words of the Phoenix New 
Times: “Arizona has agreed to step aside and see how this technology develops. If something 
goes wrong, well, there's no plan for that yet.”109 This regulatory accountability gap is clearly visible 
in the Uber death case, apparently caused by a combination of corporate expedience (disabling 
the automatic braking system) and backup driver distraction.110 
 
While autonomous vehicles arguably present AI’s most straightforward non-military dangers to 
human safety, other AI domains also raise serious concerns. For example, IBM’s Watson for 
Oncology is already being tested in hospitals across the globe, assisting in patient diagnostics 
and clinical care. Increasingly, its effectiveness, and the promises of IBM’s marketing, are being 
questioned. Investigative reporters gained access to internal documents that paint a troubling  
picture of IBM’s system, including its recommending “unsafe and incorrect cancer treatments.” 
While this system was still in its trial phase, it raised serious concerns about the incentives driving 
the rush to integrate such technology, and the lack of clinical validation and peer-reviewed 
research attesting to IBM’s marketing claims of effectiveness.111  
 
Such events have not slowed AI deployment in healthcare. Recently, the U.S. Food and Drug 
Administration (FDA) issued a controversial decision to clear the new Apple Watch, which 
features a built-in electrocardiogram (EKG) and the ability to notify a user of irregular heart 
23 

 rhythm, as safe for consumers.112 Here, concerns that the FDA may be moving too quickly in an 
attempt to keep up with the pace of innovation have joined with concerns around data privacy and 
security.113 Similarly, DeepMind Health’s decision to move its Streams Application, a tool designed 
to support decision-making by nurses and health practitioners, under the umbrella of Google, 
caused some to worry that DeepMind’s promise to not share the data of patients would be 
broken.114 
 
Children and young adults are frequently subjects of such experiments. Earlier this year, it was 
revealed that Pearson, a major AI-education vendor, inserted “social-psychological interventions” 
into one of its commercial learning software programs to test how 9,000 students would respond. 
They did this without the consent or knowledge of students, parents, or teachers.115 The company 
then tracked whether students who received “growth-mindset” messages through the learning 
software attempted and completed more problems than students who did not. This psychological 
testing on unknowing populations, especially young people in the education system, raises 
signiﬁcant ethical and privacy concerns. It also highlights the growing inﬂuence of private 
companies in purportedly public domains, and the lack of transparency and due process that 
accompany the current practices of AI deployment and integration. 
 
Here we see not only examples of the real harms that can come from biased and inaccurate AI 
systems, but evidence of the AI industry’s willingness to conduct early releases of experimental 
tools on human populations. As Amazon recently responded when criticized for monetizing 
people’s wedding and baby registries with deceptive advertising tactics, “we’re constantly 
experimenting.”116 This is a repeated pattern when market dominance and proﬁts are valued over 
safety, transparency, and assurance. Without meaningful accountability frameworks, as well as 
strong regulatory structures, this kind of unchecked experimentation will only expand in size and 
scale, and the potential hazards will grow. 
 
 

2. EMERGING SOLUTIONS IN 2018 
 

2.1 Bias Busting and Formulas for Fairness: the Limits of 
Technological “Fixes” 
 
Over the past year, we have seen growing consensus that AI systems perpetuate and amplify 
bias, and that computational methods are not inherently neutral and objective. This recognition 
comes in the wake of a string of examples, including evidence of bias in algorithmic pretrial risk 
assessments and hiring algorithms, and has been aided by the work of the Fairness,  
Accountability, and Transparency in Machine Learning community.117 The community has been at 
the center of an emerging body of academic research on AI-related bias and fairness, producing 
insights into the nature of these issues, along with methods aimed at remediating bias. These 
approaches are now being operationalized in industrial settings.  
24 

  
In the search for “algorithmic fairness”, many deﬁnitions of fairness, along with strategies to 
achieve it, have been proposed over the past few years, primarily by the technical community.118 
This work has informed the development of new algorithms and statistical techniques that aim to 
diagnose and mitigate bias. The success of such techniques is generally measured against one or 
another computational deﬁnition of fairness, based on a mathematical set of results. However, 
the problems these techniques ultimately aim to remedy have deep social and historical roots, 
some of which are more cleanly captured by discrete mathematical representations than others. 
Below is a brief survey of some of the more prominent approaches to understanding and deﬁning 
issues involving algorithmic bias and fairness. 
 
● Allocative harms describe the effects of AI systems that unfairly withhold services, 
resources, or opportunities from some. Such harms have captured much of the attention 
of those dedicated to building technical interventions that ensure fair AI systems, in part 
because it is (theoretically) possible to quantify such harms and their remediation.119 
However, we have seen less attention paid to ﬁxing systems that amplify and reproduce 
representational harms: the harm caused by systems that reproduce and amplify harmful 
stereotypes, often doing so in ways that mirror assumptions used to justify discrimination 
and inequality. 
 
In a keynote of the 2017 Conference on Neural Information Processing (NeurIPS), AI Now 
cofounder Kate Crawford described the way in which historical patterns of discrimination 
and classiﬁcation, which often construct harmful representations of people based on 
perceived differences, are reﬂected in the assumptions and data that inform AI systems, 
often resulting in allocative harms.120 This perspective requires one to move beyond 
locating biases in an algorithm or dataset, and to consider “the role of AI in harmful 
representations of human identity,” and the way in which such harmful representations are 
both shaped, and shape, our social and cultural understandings of ourselves and each 
other.121 
 
● Observational fairness strategies attempt to diagnose and mitigate bias by considering a 
dataset (either data used for training an AI model, or the input data processed by such a 
model), and applying methods to the data aimed at detecting whether it encodes bias 
against individuals or groups based on characteristics such as race, gender, or 
socioeconomic standing. These characteristics are typically referred to as protected or 
sensitive attributes. The majority of observational fairness approaches can be categorized 
as being a form of either anti-classiﬁcation, classiﬁcation parity, or calibration, as 
proposed by Sam Corbett-Davies and Sharad Goel.122 Observational fairness strategies 
have increasingly emerged through efforts from the community to contend with the 
limitations of technical fairness work and to provide entry points for other disciplines.123 
 
● Anti-classiﬁcation strategies declare a machine learning model to be fair if it does not 
depend on protected attributes in the data set. For instance, this strategy considers a 
25 

 pretrial risk assessment of two defendants who differ based on race or gender but are 
identical in terms of their other personal information to be “fair” if they are assigned the 
same risk. This strategy often requires omitting all protected attributes and their “proxies” 
from the data set that is used to train a model (proxies being any attributes that are 
correlated to protected attributes, such as ZIP code being correlated with race).124  
 
● Classiﬁcation parity declares a model fair when its predictive performance is equal across 
groupings that are deﬁned by protected attributes. For example, classiﬁcation parity would 
ensure that the percentage of people an algorithm turns down for a loan when they are 
actually creditworthy (its “false negative” rate) is the same for both Black and white 
populations. In practice, this strategy often results in decreasing the “accuracy” for certain 
populations in order to match that of others. 
 
● Calibration strategies look less at the data and more at the outcome once an AI system 
has produced a decision or prediction. These approaches work to ensure that outcomes 
do not depend on protected attributes. For example, in the case of pretrial risk 
assessment, applying a calibration strategy would aim to make sure that among a pool of 
defendants with a similar risk score, the proportion who actually do reoffend on release is 
the same across different protected attributes, such as race. 
 
Several scholars have identiﬁed limitations with these approaches to observational fairness. With 
respect to anti-classiﬁcation, some argue that there are important cases where protected 
attributes—such as race or gender—should be included in data used to train and inform an AI 
system in order to ensure equitable decisions.125 For example, Corbett-Davies and Goel discuss 
the importance of including gender in pretrial risk assessment. As women reoffend less often 
than men in many jurisdictions, gender-neutral risk assessments tend to overstate the recidivism 
risk of women, “which can lead to unnecessarily harsh judicial decisions.” As a result, some 
jurisdictions use gender-speciﬁc risk assessment tools. These cases counter a widespread view 
that deleting suﬃcient information from data sets will eventually “debias” an AI system. Since 
correlations between variables in a dataset almost always exist, removing such variables can 
result in very little information, and thus poor predictive performance without the ability to 
measure potential harms post hoc.  
 
Secondly, some have argued that different mathematical fairness criteria are mutually exclusive. 
Hence, it is generally not possible, except in highly constrained cases, to simultaneously satisfy 
both calibration and any form of classiﬁcation parity.126 These “impossibility results” show how 
each fairness strategy makes implicit assumptions about what is and is not fair. They also 
highlight the inherent mathematical trade-offs facing those aiming to mitigate various forms of 
bias based on one or another fairness deﬁnition. Ultimately, these ﬁndings serve to complicate the 
broader policy debate focused on solving bias issues with mathematical fairness tools. What they 
make clear is that solving complex policy issues related to bias and discrimination by 
indiscriminately applying one or more fairness metrics is unlikely to be successful. This does not 
mean that such metrics are not useful: observational criteria may help understanding around 
26 

 whether datasets and AI systems meet various notions of fairness and bias and subsequently 
help inform a richer discussion about the goals one hopes to achieve when deploying AI systems 
in complex social contexts.  
 
The proliferation of observational fairness methods also raises concerns over the potential to 
provide a false sense of assurance. While researchers often have a nuanced sense of the 
limitations of their tools, others who might implement them may ignore such limits when looking 
for quick ﬁxes. The idea that, once “treated” with such methods, AI systems are free of bias and 
safe to use in sensitive domains can provide a dangerous sense of false security—one that relies 
heavily on mathematical deﬁnitions of fairness without looking at the deeper social and historical 
context. As legal scholar Frank Pasquale observes, “algorithms alone can’t meaningfully hold 
other algorithms accountable.”127 
 
While increased attention to the problems of fairness and bias in AI is a positive development, 
some have expressed concern over a “mathematization of ethics.”128 As Shira Mitchell has argued: 
  
“As statistical thinkers in the political sphere we should be aware of the hazards of 
supplanting politics by an expert discourse. In general, every statistical intervention to 
a conversation tends to raise the technical bar of entry, until it is reduced to a 
conversation between technical experts…are we speaking statistics to power? Or are 
we merely providing that power with new tools for the marginalization of unquantiﬁed 
political concerns?”129  
 
Such concerns are not new. Upcoming work by Hutchinson and Mitchell surveys over ﬁfty years 
of attempts to construct quantitative fairness deﬁnitions across multiple disciplines. Their work 
recalls a period between 1964 and 1973 when researchers focused on deﬁning fairness for 
educational assessments in ways that echo the current AI fairness debate. Their efforts stalled 
after they were unable to agree on “broad technical solutions to the issues involved in fairness.” 
These precedents emphasize what the Fairness, Accountability and Transparency in Machine 
Learning community has been discovering: without a “tight connection to real world impact,” the 
added value of new fairness metrics and algorithms in the machine learning community could be 
minimal.130 In order to arrive at more meaningful research on fairness and algorithmic bias, we 
must continue to pair the expertise and perspectives of communities outside of technical 
disciplines to those within. 
 

Broader approaches 
 
Dobbe et al. have drawn on the deﬁnition of bias proposed in the early value-sensitive design 
(VSD) literature to propose a broader view of fairness.131 VSD, as theorized in the nineties by Batya 
Friedman and Helen Nissenbaum, asserts that bias in computer systems pre-exists the system 
itself.132 Such bias is reﬂected in the data that informs the systems and embedded in the 
assumptions made during the construction of a computer system. This bias manifests during the 
27 

 operation of the systems due to feedback loops and dissonance between the system and our 
dynamic social and cultural contexts.133 The VSD approach is one way to bring a broader lens to 
these issues, emphasizing the interests and perspectives of direct and indirect stakeholders 
throughout the design process.  
 
Another approach is a “social systems analysis” ﬁrst described by Kate Crawford and Ryan Calo in 
Nature.134 This is a method that combines quantitative and qualitative research methods by 
forensically analyzing a technical system while also studying the technology once it is deployed in 
social settings. It proposes that we engage with social impacts at every stage—conception, 
design, deployment, and regulation of a technology, across the life cycle.  
 
We have also seen increased focus on examining the provenance and construction of the data 
used to train and inform AI systems. This data shapes AI systems’ “view of the world,” and an 
understanding of how it is created and what it is meant to represent is essential to understanding 
the limits of the systems that it informs.135 As an initial remedy to this problem, a group of 
researchers led by Timnit Gebru proposed “Datasheets for Datasets,” a standardized form of 
documentation meant to accompany datasets used to train and inform AI systems.136 A follow-up 
paper looks at standardizing provenance for AI models.137 These approaches allow AI 
practitioners and those overseeing and assessing the applicability of AI within a given context to 
better understand whether the data that shapes a given model is appropriate, representative, or 
potentially possessing legal or ethical issues. 
 
Advances in bias-busting and fairness formulas are strong signs that the ﬁeld of AI has accepted 
that these concerns are real. However, the limits of narrow mathematical models will continue to 
undermine these approaches until broader perspectives are included. Approaches to fairness and 
bias must take into account both allocative and representational harms, and those that debate 
the deﬁnitions of fairness and bias must recognize and give voice to the individuals and 
communities most affected.138 Any formulation of fairness that excludes impacted populations 
and the institutional context in which a system is deployed is too limited. 
 

2.2 Industry Applications: Toolkits and System Tweaks 
 
This year, we have also seen several technology companies operationalize fairness deﬁnitions, 
metrics, and tools. In the last year, four of the biggest AI companies released bias mitigation tools. 
IBM released the “AI Fairness 360” open-source tool kit, which includes nine different algorithms 
and many other fairness metrics developed by researchers in the Fairness, Accountability and 
Transparency in Machine Learning community. The toolkit is intended to be integrated into the 
software development pipeline from early stages of data pre-processing, to the training process 
itself, through the use of speciﬁc mathematical models that deploy bias mitigation strategies.139 
Google’s People + AI Research group (PAIR) released the open-source “What-If” tool, a dashboard 
allowing researchers to visualize the effects of different bias mitigation strategies and metrics, as 
well as a tool called “Facets” that supports decision-making around which fairness metric to 
28 

 use.140 Microsoft released fairlearn.py, a Python package meant to help implement a binary 
classiﬁer subject to a developer’s intended fairness constraint.141 Facebook announced the 
creation and testing of a tool called “Fairness Flow”, an internal tool for Facebook engineers that 
incorporates many of the same algorithms to help identify bias in machine learning models.142 
Even Accenture, a consulting ﬁrm, has developed internal software tools to help clients 
understand and “essentially eliminate the bias in algorithms.”143 
 
Industry standards bodies have also taken on fairness efforts in response to industry and public 
sector requests for accountability assurances. The Institute of Electrical and Electronics 
Engineers (IEEE) recently announced an Ethics Certiﬁcation Program for Autonomous and 
Intelligent Systems in the hopes of creating “marks” that can attest to the broader public that an 
AI system is transparent, accountable, and fair.144 While this effort is new, and while IEEE has not 
published the certiﬁcation’s underlying methods, it is hard to see, given the complexity of these 
issues, how settling on one certiﬁcation standard across all contexts and all AI systems would be 
possible—or ultimately reliable—in ensuring that systems are used in safe and ethical ways. 
Similar concerns have arisen in other contexts, such as privacy certiﬁcation programs.145 
 
In both the rapid industrial adoption of academic fairness methods, and the rush to certiﬁcation, 
we see an eagerness to “solve” and “eliminate” problems of bias and fairness using familiar 
approaches and skills that avoid the need for signiﬁcant structural change, and which fail to 
interrogate the complex social and historical factors at play. Combining “academically credible” 
technical fairness ﬁxes and certiﬁcation check boxes runs the risk of instrumenting fairness in 
ways that lets industry say it has ﬁxed these problems and may divert attention from examining 
ongoing harms. It also relieves companies of the responsibility to explore more complex and 
costly forms of review and remediation. Rather than relying on quick ﬁxes, tools, and 
certiﬁcations, issues of bias and fairness require deeper consideration and more robust 
accountability frameworks, including strong disclaimers about how “automated fairness” cannot 
be relied on to truly eliminate bias from AI systems. 
 

2.3 Why Ethics is Not Enough 
 
A top-level recommendation in the AI Now 2017 Report advised that “ethical codes meant to steer 
the AI ﬁeld should be accompanied by strong oversight and accountability mechanisms.”146 While 
we have seen a rush to adopt such codes, in many instances offered as a means to address the 
growing controversy surrounding the design and implementation of AI systems, we have not seen 
strong oversight and accountability to backstop these ethical commitments.  
 
After it was revealed that Google was working with the Pentagon on Project Maven—developing AI 
systems for drone surveillance—the debate about the role of AI in weapons systems grew in 
intensity. Project Maven generated signiﬁcant protest among Google’s employees, who 
successfully petitioned the company’s leadership to end their involvement with the program when 
the current contract expired.147 By way of response, Google’s CEO Sundar Pichai released a public 
29 

 set of seven “guiding principles” designed to ensure that the company’s work on AI will be socially 
responsible.148 These ethical principles include the commitment to ”be socially beneﬁcial,” and to 
“avoid creating or reinforcing unfair bias.” They also include a section titled, “AI applications we will 
not pursue,” which includes “weapons and other technologies whose principal purpose or 
implementation is to cause or directly facilitate injury to people”—a direct response to the 
company’s decision not to renew its contract with the Department of Defense. But it is not clear to 
the public who would oversee the implementation of the principles, and no ethics board has been 
named.  
 
Google was not alone. Other companies, including Microsoft, Facebook, and police body camera 
maker Axon, also assembled ethics boards, advisors, and teams.149 In addition, technical 
membership organizations moved to update several of their ethical codes. The IEEE reworked its 
code of ethics to reﬂect the challenges of AI and autonomous systems, and researchers in the 
Association for Computing Machinery (ACM) called for a restructuring of peer review processes, 
requiring the authors of technical papers to consider the potential adverse uses of their work, 
which is not a common practice.150 Universities including Harvard, NYU, Stanford, and MIT offered 
new courses on the ethics and ethical AI development practices aimed at identifying issues and 
considering the ramiﬁcations of technological innovation before it is implemented at scale.151 The 
University of Montreal launched a wide-ranging process to formulate a declaration for the 
responsible development of AI that includes both expert summits and open public deliberations 
for input from citizens.152  
 
Such developments are encouraging, and it is noteworthy that those at the heart of AI 
development have declared they are taking ethics seriously. Ethical initiatives help develop a 
shared language with which to discuss and debate social and political concerns. They provide 
developers, company employees, and other stakeholders a set of high-level value statements or 
objectives against which actions can be later judged. They are also educational, often doing the 
work of raising awareness of particular risks of AI both within a given institution, and externally, 
amongst the broader concerned public.153 
 
However, developing socially just and equitable AI systems will require more than ethical 
language, however well-intentioned it may be. We see two classes of problems with this current 
approach to ethics. The ﬁrst has to do with enforcement and accountability. Ethical approaches in 
industry implicitly ask that the public simply take corporations at their word when they say they 
will guide their conduct in ethical ways. While the public may be able to compare a post hoc 
decision made by a company to its guiding principles, this does not allow insight into decision 
making, or the power to reverse or guide such a decision. In her analysis of Google’s AI Principles, 
Lucy Suchman, a pioneering scholar of human computer interaction, argues that without “the 
requisite bodies for deliberation, appeal, and redress” vague ethical principles like “don’t be evil” or 
“do the right thing” are “vacuous.”154  
 
This “trust us” form of corporate self-governance also has the potential to displace or forestall 
more comprehensive and binding forms of governmental regulation. Ben Wagner of the Vienna 
30 

 University of Economics and Business argues, “Unable or unwilling to properly provide regulatory 
solutions, ethics is seen as the “easy” or “soft” option which can help structure and give meaning 
to existing self-regulatory initiatives.”155 In other words, ethical codes may deﬂect criticism by 
acknowledging that problems exist, without ceding any power to regulate or transform the way 
technology is developed and applied. The fact that a former Facebook operations manager 
claims, “We can’t trust Facebook to regulate itself,” should be taken into account when evaluating 
ethical codes in industry.156 
 
A second problem relates to the deeper assumptions and worldviews of the designers of ethical 
codes in the technology industry. In response to the proliferation of corporate ethics initiatives, 
Greene et al. undertook a systematic critical review of high-proﬁle “vision statements for ethical 
AI.”157 One of their ﬁndings was that these statements tend to adopt a technologically 
deterministic worldview, one where ethical agency and decision making was delegated to experts, 
“a narrow circle of who can or should adjudicate ethical concerns around AI/ML” on behalf of the 
rest of us. These statements often assert that AI promises both great beneﬁts and risks to a 
universal humanity, without acknowledgement of more speciﬁc risks to marginalized populations. 
Rather than asking fundamental ethical and political questions about whether AI systems should 
be built, these documents implicitly frame technological progress as inevitable, calling for better 
building.158  
 
Empirical study of the use of these codes is only beginning, but preliminary results are not 
promising. One recent study found that “explicitly instructing [engineers] to consider the ACM 
code of ethics in their decision making had no observed effect when compared with a control 
group.”159 However, these researchers did ﬁnd that media or historical accounts of ethical 
controversies in engineering, like Volkswagen’s Dieselgate, may prompt more reﬂective practice. 
 
Perhaps the most revealing evidence of the limitations of these emerging ethical codes is how 
corporations act after they formulate them. Among the list of applications Google promises not to 
pursue as a part of its AI Principles are “technologies whose purpose contravenes widely 
accepted principles of international law and human rights.”160 That was tested earlier this year 
after investigative journalists revealed that Google was quietly developing a censored version of 
its search engine (which relies extensively on AI capabilities) for the Chinese market, code-named 
Dragonﬂy.161 Organizations condemned the project as a violation of human rights law, and as 
such, a violation of Google’s AI principles. Google employees also organized against the effort.162 
As of writing, the project has not been cancelled, nor has its continued development been 
explained in light of the clear commitment in the company’s AI Principles, although Google’s CEO 
has defended it as “exploratory.”163 
 
There is an obvious need for accountability and oversight in the industry, and so far the move 
toward ethics is not meeting this need. This is likely in part due to the market-driven incentives 
working against industry-driven implementations: a drastic (if momentary) drop in Facebook and 
Twitter’s share price occurred after they announced efforts to combat misinformation and 
increase spending on security and privacy efforts.164  
31 

  
This is no excuse not to pursue a more ethically driven agenda, but it does suggest that we should 
be wary of relying on companies to implement ethical practices voluntarily, since many of the 
incentives governing these large, publicly traded technology corporations penalize ethical action. 
For these mechanisms to serve as meaningful forms of accountability requires that external 
oversight and transparency be put into place to ensure that there exists an external system of 
checks and balances in addition to the cultivation of ethical norms and values within the 
engineering profession and technology companies. 
 
 

3. WHAT IS NEEDED NEXT 
 
When we released our AI Now 2016 Report, fairness formulas, debiasing toolkits, and ethical 
guidelines for AI were rare. The fact that they are commonplace today shows how far the ﬁeld has 
come. Yet much more needs to be done. Below, we outline seven strategies for future progress on 
these issues. 
 

3.1 From Fairness to Justice 
 
Any debate about bias and fairness should approach issues of power and hierarchy, looking at 
who is in a position to produce and proﬁt from these systems, whose values are embedded in 
these systems, who sets their “objective functions,” and which contexts they are intended to work 
within.165 Echoing the Association for Computing Machinery (ACM) researcher’s call for an 
acknowledgement of “negative implications” as a requirement for peer review, much more 
attention must be paid to the ways that AI can be used as a tool for exploitation and control.166 We 
must also be cautious not to reframe political questions as technical concerns.167  
 
When framed as technical “ﬁxes,” debiasing solutions rarely allow for questions about the 
appropriateness or eﬃcacy of an AI system altogether, or for an interrogation of the institutional 
context into which the “ﬁxed” AI system will ultimately be applied. For example, a “debiased” 
predictive algorithm that accurately forecasts where crime will occur, but that is being used by law 
enforcement to harass and oppress communities of color, is still an essentially unfair system.168 
To this end, our deﬁnitions of “fairness” must expand to encompass the structural, historical, and 
political contexts in which an algorithmic systems is deployed.  
 
Furthermore, fairness is a term that can be easily co-opted: important questions such as “Fair to 
whom? And in what context?” should always be asked. For example, making a facial recognition 
system perform equally on people with light and dark skin may be a type of technical progress in 
terms of parity, but if that technology is disproportionately used on people of color and 
low-income communities, is it really “fair?” This is why deﬁnitions of fairness face a hard limit if 
they remain purely contained within the technical domain: in short, “parity is not justice.”169 
32 

  

3.2 Infrastructural Thinking 
 
In order to better understand and track the complexities of AI systems, we need to look beyond 
the technology and the hype to account for the broader context of how AI is shaping and shaped 
by social and material forces. As Edwards et al. argue: “When dealing with infrastructures, we 
need to look to the whole array of organizational forms, practices, and institutions which 
accompany, make possible, and inﬂect the development of new technology.”170 Doing so requires 
both experimental methodological approaches and theory building, expanding beyond narrow 
analyses of individual systems in isolation to consider them on a local and global scale. It also 
requires considering ways in which technologies are entangled in social relations, material 
dependencies, and political purposes.171 
 
In “Anatomy of an AI System,” a 2018 essay and large-scale map, AI Now cofounder Kate 
Crawford and Professor Vladan Joler took a single Amazon Echo and analyzed all the forms of 
environmental and labor resources required to develop, produce, maintain, and ﬁnally dispose of 
this sleek and seemingly simple object. When you ask Alexa to play your favorite song, you have 
drawn on a massive interlinked chain of extractive processes. It involves lithium mining in Bolivia, 
clickworkers creating large-scale training datasets in southeast Asia, container ships and 
international logistics, and vast data extraction and analysis by Alexa Voice Service (AVS) across 
distributed data centers. The process ends in the ﬁnal resting place of all AI consumer gadgets: in 
e-waste rubbish heaps in Ghana, Pakistan, and China.  
 
The “Anatomy of an AI System” project points to approaches we can employ in contending with 
the global implications of AI, and the multi-layered nature of value extraction and exploitation from 
the developing world to the developed world. This helps to illuminate the darker corners that are 
rarely considered in analysis of AI systems.172  
 
In particular, an infrastructural analysis of AI shows that there are black boxes within black boxes: 
not just at the algorithmic level, but also at the levels of trade secrecy laws, labor practices, and 
untraceable global supply chains for rare earth minerals used to build consumer AI devices. These 
obscure not only the material impacts of AI systems, but the intensive human work of maintaining 
and repairing them through practices like content moderation and data training.173 As Nick Seaver 
puts it, “If you cannot see a human in the loop, you just need to look for a bigger loop.”174  
 
Only by tracing across these sociotechnical layers can we understand what we are calling the “full 
stack supply chain” of AI—the human and nonhuman components that make up the global scale 
of AI systems. There are many sociotechnical data infrastructures needed to make AI function: 
these include training data, test data, APIs, data centers, ﬁber networks, undersea cables, energy 
use, labor involved in content moderation and training set creation, and a constant reliance on  
 
 
33 

 clickwork to develop and maintain AI systems. We cannot see the global environmental and labor 
implications of these tools of everyday convenience, nor can we meaningfully advocate for 
fairness, accountability, and transparency in AI systems, without an understanding of this full 
stack supply chain. 
 

3.3 Accounting for Hidden Labor in AI Systems 
 
Another emerging research area where we expect to see greater impact focuses on the underpaid 
and unrecognized workers who help build, maintain, and test AI systems. This hidden human 
labor takes many forms—from supply chain work, to digital crowdsourced “clickwork,” to 
traditional service industry jobs. Hidden labor exists at all stages of the AI pipeline, from 
producing and transporting the raw minerals required to create the core infrastructure of AI 
systems, to providing the invisible human work that often backstops claims of AI “magic” once 
these systems are deployed in products and services.175 Communications scholar Lilly Irani refers 
to such hidden labor as “human-fueled automation.”176 Her research draws attention to the 
experiences of clickworkers or “microworkers” who perform the repetitive digital tasks that 
underlie AI systems, like labeling training data and reviewing ﬂagged content, as “workers hidden 
in the technology.”177 
 
While this labor is essential to making AI systems “work,” it is usually very poorly compensated. A 
2018 study from the United Nations’ International Labor Organization (ILO) surveyed 3,500 
microworkers from 75 countries who routinely offered their labor on popular microtask platforms 
like Mechanical Turk, Crowdﬂower, Microworker, and Clickworker. The report found that a 
substantial number of people earned below their local minimum wage (despite 57% of 
respondents having advanced degrees specializing in science and technology).178 Similarly, those 
who do content moderation work, screening problematic content posted on social media 
platforms and news feeds, are also paid poorly, in spite of their essential and emotionally diﬃcult 
labor.179 
 
This has not been lost on some in the technical AI research community, who have begun to call 
attention to the crucial and marginalized role of this labor, and to consider their own responsibility 
to intervene. Silberman and others discuss how researchers conducting AI studies are 
increasingly dependent upon cheap crowdsourced labor.180 They note that, between the years 
2008 and 2016, the term “crowdsourcing” went from appearing in less than 1,000 scientiﬁc 
articles to over 20,000. With online microworkers unregulated by current labor laws, researchers 
are being asked to reconsider what counts as “ethical conduct” in the AI research community. 
Silberman et al. argue for treating crowdworkers as coworkers, paying them minimum wage 
determined by the client’s location, and the need for additional Institutional Review Board (IRB) 
oversight. 
 
 
 

34 

 The practice of examining hidden human labor draws on a lineage of feminist research. The 
concept of “invisible work,” for instance, originated with studies of unpaid women’s care work and 
investigations into organizational settings that relied upon “emotional labor,” particularly 
traditionally “feminized” ﬁelds like nursing and ﬂight attendants.181 Researchers found that 
common activities taken on by female workers, such as soothing anxious patients or managing 
unruly customers, were not formally recognized or compensated as work, in spite of their being 
essential. The feminist legacy of invisible work is useful for contextualizing these new forms of 
labor, and in understanding the characterization of this work, which, while essential, is often 
written out of the AI narrative, rarely counted or compensated. 
 
In her article, “The Automation Charade,” Astra Taylor proposes the term “fauxtomation” to call 
attention to the gap between the marketing rhetoric of AI as a seamless product or service and 
the messy, lived reality of automation, which frequently relies on such unsung human labor. 
“Automation,” Taylor cautions, “has an ideological function as well as a technological 
dimension.”182 In making this case, she critiques popular narratives around the future of labor, 
which posit a near-horizon where workers will be replaced by machines. She sees such claims as 
functioning to disempower workers: what leverage do workers have to demand better wages and 
beneﬁts in the face of impending automation? We saw this narrative deployed in 2016 by former 
McDonald’s CEO Ed Rensi, who cited the growing “Fight for $15” movement as the impetus for the 
company’s introduction of automated kiosks to replace cashiers.183 Workers who fought for better 
pay would ultimately be worse off, he reasoned, as their demand for living wages would force the 
company to automate and eliminate them. Examining his claim two years on, we see that this is 
not entirely true. Automation or no, workers are still needed: after McDonald’s added kiosks to its 
Chicago ﬂagship store, the location reopened with more employees than before the kiosks were 
introduced.184 
 
The integration of automation and AI in the workplace is aimed not only at automating worker 
tasks, but at managing, monitoring, and assessing workers themselves. Alex Rosenblatt’s 2018 
ethnography of Uber drivers details the precarity and uncertainty produced by depending on the 
whims of a centralized, AI-enabled platform for one’s livelihood. The algorithmic logic that governs 
ride-sharing applications can arbitrarily bar drivers from work, result in unreliable wages and 
unexpected costs, and nudge people into working longer hours, resulting in unsafe driving 
conditions.185 Such platforms isolate workers from each other, making concerted activity and 
labor organizing diﬃcult. They also function to create signiﬁcant information asymmetries 
between data-rich companies aiming to extract value from workers, and the workers themselves. 
Even so, 2018 has seen increasing dissent from such workers. Some prominent examples of 
worker-driven protest include on-demand delivery riders striking alongside UK fast food industry 
employees and rideshare drivers calling for job protections.186  
 
Silicon Valley contractors working in security, food, and janitorial services within major technology 
companies have also organized, seeking a living wage and other protections.187 They are among 
thousands of workers who labor alongside their full-time technology worker peers, but are 
classiﬁed as independent contractors. Under this designation, they are often paid low wages, and 
35 

 provided few beneﬁts and protections. They are also rarely counted in oﬃcial employee numbers, 
even though they make up a large portion of most technology industry workforces, and perform 
essential work. For example, as of this year, contract workers outnumber Google’s direct 
employees for the ﬁrst time in the company’s history.188 This increasing wave of dissent makes 
visible the social tensions at the heart of the practice of hiding and marginalizing important forms 
of labor.  
 
The physical, emotional, and ﬁnancial costs of treating workers like “bits of code” and devaluing 
their work and well-being has been highlighted in recent news articles describing the conditions of 
Amazon warehouse workers and contracted Prime delivery drivers.189 Amazon warehouse 
workers recently went on strike in Europe, protesting harsh conditions. According to one striking 
worker, “You start at the company healthy and leave it as a broken human,” with many workers 
requiring surgeries related to workplace conditions.190  
 
Recognizing all of the labor required to “make AI work” can help us better understand the 
implications of its development and use. Research in these areas also helps us reexamine the 
focus on technical talent in narratives describing AI’s creation and recognize that technical skills 
account for only a portion of a much larger effort. This enables us to question numerous labor 
policies, such as the focus on pushing workers to acquire coding or data science skills as a way 
to ensure they are counted and compensated. They also help us identify who is likely to beneﬁt, 
and who, along the AI production and deployment pipeline, is likely to be harmed. 
 

3.4 Deeper Interdisciplinarity 
 
AI researchers and developers are engaged in building technologies that have signiﬁcant 
implications for diverse populations in broad ﬁelds like law, sociology, and medicine. Yet much of 
this development happens far removed from the experience and expertise of these groups. This 
has led to a call to expand the disciplinary makeup of those engaged in AI design, development, 
and critique, beyond purely technical expertise.191 Since then, we have seen some movement in 
this direction. Recently, MIT announced plans to establish a new college of computing that aims 
to “advance pioneering work on AI’s ethical use and societal impact” by fostering integrated 
cross-disciplinary training, “educating the bilinguals of the future,” as MIT President L. Rafael Reif 
described it.192  
 
Such initiatives are critical: as AI becomes more deeply embedded in areas like healthcare, 
criminal justice, hiring, housing, and educational systems, experts from these domains are 
essential if we are to ensure AI works as envisioned. In integrating these disciplinary perspectives, 
it is important that they are not merely ”languages” to be acquired by computer scientists and 
engineers seeking to expand their work into new areas—especially when other disciplines have 
been leading that work. Instead, social science and the humanities should be centered as 
contributors to the AI ﬁeld’s foundational knowledge and future direction, enabling us to leverage 
new modes of analysis and methodological intervention.193  
36 

  

3.5 Race, Gender and Power in AI 
 
This year, a groundswell of political action emerged around issues of discrimination, harassment, 
and inequity in the technology industry, especially in the AI ﬁeld.194 This rising concern weaves 
together a number of related issues, from the biases in AI systems, to failed diversity and 
inclusion efforts within industry and academia, to the grassroots efforts to confront sexual 
harassment and the abuse of power in workplaces and classrooms.  
 
Resonating with the broader #MeToo movement, we saw issues relating to diversity and inclusion 
in artiﬁcial intelligence rise on the public agenda:  
 
● Following the 2017 Conference on Neural Information Processing Systems, members of 
the artiﬁcial intelligence and machine learning communities began voicing concerns about 
long standing problems of harassment and discrimination in conference settings, leading 
to #ProtestNIPS, a movement aimed at highlighting examples of toxicity in the community 
and the need to address them.195 Among other things, this provoked a change to the 
conference acronym, a longstanding subject of sensitivity for its gendered and historical 
connotations. The conference, which was previously referred to as NIPS, now goes by 
NeurIPS.196 
 
● We also saw renewed focus on initiatives devoted to creating platforms for inclusion in 
the ﬁeld, such as Black in AI, Women in Machine Learning, Latinx in AI, and Queer in AI, 
alongside the appointment of Diversity and Inclusion chairs and a series of other changes 
to the design of NeurIPS intended to foster equity and inclusion among participants.197 
 
● Across the industry, we saw a growing technology worker movement that intersected with 
these issues. The Google Walkout, in particular, took on a worker-driven agenda that 
acknowledged that race, class, and sexuality are intertwined with forms of gender-based 
discrimination. The walkout explicitly aimed to center the needs of the company’s 
temporary contract workers and vendors who lack the job security and beneﬁts of more 
privileged technology workers.198 These efforts have led to some signiﬁcant structural 
changes—notably, the end to forced arbitration for sexual harassment claims across a 
number of the largest companies in the AI industry.199  
 
● In other arenas, corporate boards have ignored or otherwise refused to address 
shareholder proposals targeting discriminatory workspaces. This year, Google dismissed 
a proposal that would tie executive compensation to progress made on diversity and 
inclusion, while in 2016, Apple refused a mandate that would require it to diversify its 
board and senior management.200 
 
37 

 Across these efforts, advocates of diversity in AI are ﬁnding intersections between the move to 
address gender and race-based harassment and abuse within the technology community, and 
other forms of inequity and abuses of power. But this is still an uphill battle: while there is 
increased attention to problems of bias in AI systems, we have yet to see much research within 
the fairness and bias debate focused on the state of equity and diversity in the AI ﬁeld itself. 
Indeed, reliable ﬁgures on representation in AI are diﬃcult to come by, although some limited data 
does exist.  
 
A recent estimate produced by WIRED and Element AI found that only 12% of researchers who 
contributed to the three leading machine learning conferences in 2017 were women. This gender 
gap is replicated at large technology ﬁrms like Facebook and Google, whose websites show that 
only 15% and 10% of their AI research staff are women.201 And there is no reliable data on the 
state of racial diversity in the ﬁeld, or retention rates for people of color.202 Collectively, the limited 
evidence suggests that AI, as a ﬁeld, is even less diverse than computer science as a whole, 
which is itself at a historic low point: women make up only 18% of computer science majors in the 
United States, a decline from a high point of 37% in 1984.203  
 
These trends are even more dramatic when compared to other STEM ﬁelds in which gender 
diversity has shown a marked improvement.204 Yet these are not new problems: the 
WIRED/Element AI survey is not signiﬁcantly different from a study of the AI ﬁeld that was 
published by IEEE Expert in 1992, which found that only 13% of published authors in the journal 
over the prior four years were women.205 And in the 1980s, female grad students at MIT’s 
Computer Science and Artiﬁcial Intelligence Labs thoroughly documented their experiences with 
toxic working environments in the report “Barriers to Equality in Academia: Women in Computer 
Science at MIT.”206 
 
It is time to address the connection between discrimination and harassment in the AI community, 
and bias in the technical products that are produced by the community. Scholars in science and 
technology studies have long observed that the values and beliefs of those who create 
technologies shape the technologies they create.207 Expanding the ﬁeld’s frame of reference to 
recognize this connection will ensure it is better equipped to address the problems raised by its 
rapid proliferation into sensitive social domains. As one AI researcher put it, “Bias is not just in our 
datasets, it’s in our conferences and community.”208  
 
A recent example illustrates these connections, and how discriminatory practices within the 
culture that produces an AI system can be mirrored and ampliﬁed in the system itself. Amazon 
recently developed an experimental AI system to help it rank job candidates. It trained the system 
on data reﬂecting the company’s historical hiring preferences, hoping to more eﬃciently identify 
qualiﬁed candidates.209 But the system didn’t work as expected: based on the company’s 
historical hiring, it showed a distinct bias against women candidates, downgrading resumes from 
candidates who attended two all-women’s colleges, and even penalizing resumes that contained 
the word “woman.” After uncovering this bias, the company attempted to ﬁx the system, adjusting 
the algorithm to treat these terms more fairly. This did not work, and the project was eventually 
38 

 scrapped. Gender-based discrimination was embedded too deeply within the system – a system 
built to reﬂect Amazon’s past hiring practices – to be uprooted using the “debiasing” approach 
commonly adopted within the AI ﬁeld. 
 
As scholars like Saﬁya Noble and Mar Hicks have observed, there is a clear through-line 
connecting longstanding patterns of discrimination and harassment in AI to the ways artiﬁcial 
intelligence technologies can amplify and contribute to marginalization and social inequity.210 
Patterns of cultural discrimination are often embedded in AI systems in complex and meaningful 
ways, and we need to better understand how these effects are felt by different communities.211  
 
This is a space that has too long been overlooked and where research is sorely needed. AI Now is 
publishing a dedicated report on these issues in December 2018, and we have a multi-year 
research project dedicated to examining these challenges. 
 

3.6 Strategic Litigation and Policy Interventions 
 
This year saw an increase in court challenges to the use of automated systems, particularly when 
government agencies use them in decisions that affect individual rights. In a recent AI Now 
Report called “Litigating Algorithms,” we documented ﬁve recent case studies of litigation 
involving the use of automated systems: in Medicaid and disability beneﬁts cases, public teacher 
employment evaluations, juvenile criminal risk assessment, and criminal DNA analysis.212 
The ﬁndings brought to light several emerging trends. First, these cases provided concrete 
evidence that governments are routinely adopting automated decision systems (ADS) as 
measures to produce “cost savings” or to streamline work. Yet, they are failing to assess how 
these systems might disproportionately harm the populations they are meant to serve, 
particularly those who are the most vulnerable and who have little recourse or even knowledge 
that these systems are deeply affecting their lives. In many cases, there was not a single 
government employee who could explain the automated decision, correct errors, or audit the 
results of its determination. Through a series of vendor and contractor agreements, almost all 
avenues for understanding or contesting the impact of these systems were shielded by legal 
protections such as trade secret law. 
 
Second, few government agencies had invested real efforts to ensure that fairness and due 
process protections remained in place when switching from human-driven decisions to 
algorithmically-driven ones. The typical audit, appeals, and accountability mechanisms were 
totally absent from automated system design. Fortunately, successful strategic litigation by 
lawyers from the American Civil Liberties Union (ACLU) of Idaho, Legal Aid of Arkansas, the 
Houston Federation of Teachers, The Legal Aid Society of New York, and various public defenders 
were able to secure victories for their clients and challenge these unlawful uses based, in part, on 
constitutional and administrative due process litigation claims. 
 

39 

 The playbook for how to litigate algorithms is still being written, but our report uncovered several 
useful strategies to support long-term solutions and protections. First, arguments based on 
procedural due process presented serious challenges to the trade secrecy claims of private 
vendors, with the vast majority of judges ruling that the right to assert constitutional or civil rights 
protections outweighs any risk of intellectual property misappropriation. Second, a failure to notify 
affected individuals and communities matters: agencies who neglected to engage community 
groups concerning the use of these systems were often judged to have failed to appropriately 
provide the opportunity for public notice and comment, meaning that their implementation of AI 
systems was potentially unconstitutional. Third, interdisciplinary collaboration is important when 
trying to determine where these systems fail, especially when submitting evidence to judges. In 
cases in which lawyers worked closely with technical and social science experts, judges were able 
to learn about the scientiﬁc ﬂaws in these systems as well as the social ramiﬁcations and harms. 
 
Looking forward, we anticipate future strategic litigation cases will produce many more lessons. 
These interventions generate greater understanding and remedial accountability for these 
systems, even in situations where government agencies have attempted to disclaim ownership, 
understanding, or control. Combined with tools such as AI Now’s Algorithmic Impact Assessment 
framework, alongside robust regulatory oversight regimes, we can begin to identify, measure, and, 
when necessary, intervene in efforts to use AI and automated systems in ways that produce 
harm.213 However, in order to continue to build on recent progress, lawyers and community 
activists who represent individuals in such suits need greater funding and support, as well as 
networks of domain experts that they can draw on to help advise strategy and audit systems. 

3.7 Research and Organizing: An Emergent Coalition 
The rapid deployment of AI and related systems in everyday life is not a concern for the future—it 
is already here, with no signs of slowing down. Recognizing this, a set of strategies have emerged, 
drawing on long-standing traditions of activism and organizing to demand structural changes for 
greater accountability.  
 
Social activism by technologists is nothing new. In the early 1980s, Computer Professionals for 
Social Responsibility formed to oppose the use of computers in warfare.214 More recently, the 
2016 “Never Again” technology pledge rallied thousands of workers in various technology sectors 
to sign a promise not to build databases or conduct data collection that could be used to target 
religious minorities or facilitate mass deportations.215 While 2018’s organizing and activism draws 
from a long tradition, its scale is new to the technology sector. Technology workers are joining 
forces with civil society organizations and researchers in opposition to their employers’ technical 
and business decisions.  
 
Google employees kicked off publicly visible organizing in 2018, opposing Project Maven, a 
Pentagon effort to apply Google’s machine vision AI capabilities to Department of Defense drone 
surveillance.216 Researchers and human rights organizations joined the cause, and in June, 
40 

 Google announced it would abandon the project.217 At Amazon, Salesforce, and Microsoft, 
employees petitioned their leadership to end contracts with Immigrations and Customs 
Enforcement (ICE), supported by immigration and advocacy organizations.218 Amazon employees 
also joined the ACLU in petitioning the company to stop selling facial recognition to law 
enforcement, responding to the ACLU’s work exposing existing contracts.219 Following Maven, 
Google employees again rose up against Project Dragonﬂy, a version of the Google search engine 
enabling government-directed censorship and surveillance, planned for the Chinese market.220 In 
response to media reports that disclosed the secretive effort, employees requested ethical 
oversight and accountability, and over 700 of them joined Amnesty International in a call to cancel 
the project, signing their name publicly to an open letter which coincided with Amnesty 
International protests221 
 
The biggest moment occurred in early November, when 20,000 Google workers walked out 
around the globe in an action called Walkout for Real Change.222 The walkout characterized 
Google as a company at which “abuse of power, systemic racism, and unaccountable 
decision-making are the norm.”223 Organizers called on leadership to meet ﬁve demands, including 
ending pay and opportunity inequity, eliminating forced arbitration in cases of sexual harassment 
and discrimination, and adding an employee representative to the board of directors. A week after 
the walkout, Google met a small portion of these demands, agreeing to end forced arbitration in 
cases of sexual harassment (but notably ignoring discrimination).224 This move was quickly 
replicated throughout the industry, with Facebook, Square, eBay, and Airbnb following suit.225  
 
By joining forces with researchers and civil society groups, this new wave of labor organizing 
mirrors calls for greater diversity and openness within the AI research domain.226 These 
movements are incorporating diverse perspectives across class, sector, and discipline, working to 
ensure they are capable of understanding the true costs of company practices, including the 
impact of the systems they build. The Google workers who participated in the walkout expanded 
their coalition across class and sector, emphasizing contract workers in their demands, and 
situating themselves within a growing movement “not just in tech, but across the country, 
including teachers, fast-food workers and others who are using their strength in numbers to make 
real change.”227 
 
The recent surge in activism has largely been driven by whistleblowers within technology 
companies, who have disclosed information about secretive projects to journalists.228 These 
disclosures have helped educate the public, which is traditionally excluded from such access, and 
helped external researchers and advocates provide more informed analysis. By establishing 
shared ground truth, whistleblowing has helped build the broad coalitions that characterize these 
movements. The critical role of ethical whistleblowing over the last year has also highlighted both 
its social importance, and the lack of protections for those who make such disclosures.  
 
The broad coalition of technology worker organizers, researchers, and civil society is playing an 
increasing role in the push for accountability in the technology sector. Many engineering 
employees have considerable bargaining power and are uniquely positioned to demand change 
41 

 from their employers.229 Applying this power to push for greater accountability presents a hopeful 
model for labor organizing in the public interest, especially given the current lack of government 
regulation, external oversight, and other meaningful levers capable of reviewing and steering 
technology company decision making. 
 
 

CONCLUSION  
 
This year saw AI systems rapidly introduced into more social domains, leaving increasing 
numbers of people at risk. While AI techniques still offer considerable promise, rapid deployment 
of systems without appropriate assessment, accountability, and oversight can create serious 
hazards. We urgently need to regulate AI systems sector-by-sector, with particular attention paid 
to facial and affect recognition, and to inform those policies with rigorous research.  
 
But regulation can only be effective if the legal and technological barriers that prevent auditing, 
understanding, and intervening in these systems are removed. Back in 2016, we recommended in 
the ﬁrst AI Now report that the Computer Fraud and Abuse Act (CFAA) and the Digital Millennium 
Copyright Act (DMCA) should not be used to restrict research into AI accountability and 
auditing.230 This year, we go further: AI companies should waive trade secrecy and other legal 
claims that would prevent algorithmic accountability in the public sector. Governments and public 
institutions must be able to understand and explain how and why decisions are made, particularly 
when people’s access to healthcare, housing, and employment is on the line. 
 
The question is no longer whether there are harms and biases in AI systems. That debate has 
been settled: the evidence has mounted beyond doubt in the last year. The next task now is 
addressing these harms. This is particularly urgent given the scale at which these systems are 
deployed, the way they function to centralize power and insight in the hands of the few, and the 
increasingly uneven distribution of costs and beneﬁts that accompanies this centralization. We 
need deeper analyses of the “full stack supply chain” behind AI systems, to track their 
development and deployment across the product life cycle, and to take into account their true 
environmental and labor costs.231  
 
Furthermore, it is long overdue for technology companies to directly address the cultures of 
exclusion and discrimination in the workplace. The lack of diversity and ongoing tactics of 
harassment, exclusion, and unequal pay are not only deeply harmful to employees in these 
companies but also impacts the AI products they release, producing tools that perpetuate bias 
and discrimination.232 
  
The current structure within which AI development and deployment occurs works against 
meaningfully addressing these pressing issues. Those in a position to proﬁt are incentivized to 
accelerate the development and application of systems without taking the time to build diverse 
teams, create safety guardrails, or test for disparate impacts. Those most exposed to harm from 
42 

 these systems commonly lack the ﬁnancial means and access to accountability mechanisms 
that would allow for redress or legal appeals.233 This is why we are arguing for greater funding for 
public litigation, labor organizing, and community participation as more AI and algorithmic 
systems shift the balance of power across many institutions and workplaces.  
  
It is imperative that the balance of power shifts back in the public’s favor. This will require 
signiﬁcant structural change that goes well beyond a focus on technical systems, including a 
willingness to alter the standard operational assumptions that govern the modern AI industry 
players. The current focus on discrete technical ﬁxes to systems should expand to draw on 
socially-engaged disciplines, histories, and strategies capable of providing a deeper 
understanding of the various social contexts that shape the development and use of AI systems.  
 
As more universities turn their focus to the study of AI’s social implications, computer science and 
engineering can no longer be the unquestioned center, but should collaborate more equally with 
social and humanistic disciplines, as well as with civil society organizations and affected 
communities.  
 
Fortunately, we are beginning to see new coalitions form between researchers, activists, lawyers, 
concerned technology workers, and civil society organizations to support the oversight, 
accountability, and ongoing monitoring of AI systems. For these important connections to grow, 
more protections are needed, including a commitment from technology companies to provide 
protections for conscientious objectors who do not want to work on military or policing contracts, 
along with protections for employees involved in labor organizing and ethical whistleblowers.234  
 
The last year revealed many of the hardest challenges for accountability and justice as AI 
systems moved deeper into the social world. Yet there have been extraordinary moments of 
potential, as well as signiﬁcant public debates and hopeful forms of protest, that may ultimately 
illuminate the pathways for consequential and positive change. 
 
 

43 

 ENDNOTES 
 
1.

As AI pioneers Stuart Russell and Peter Norvig point out, the history of artiﬁcial intelligence has not 
produced a clear deﬁnition of AI, but can be seen as variously emphasizing four possible goals: 
“systems that think like humans, systems that act like humans, systems that think rationally, systems 
that act rationally.” In this report we use the term AI to refer to a broad assemblage of technologies, 
from early rule-based algorithmic systems to deep neural networks, all of which rely on an array of 
data and computational infrastructures. These technologies span speech recognition, language 
translation, image recognition, predictions, and determinations—tasks that have traditionally relied on 
human capacities across the four goals Russell and Norvig identify. While AI is not new, recent 
developments in the ability to collect and store large quantities of data, combined with advances in 
computational power have led to signiﬁcant breakthroughs in the ﬁeld over the last ten years, along 
with a strong push to commercialize these technologies and apply them across core social domains. 
See: Stuart J. Russell and Peter Norvig, Artiﬁcial Intelligence: A Modern Approach, (Englewood Cliffs, 
NJ: Prentice Hall, 1995), 2. 

2.

Carole Cadwalladr and Emma Graham-Harrison, “Revealed: 50 Million Facebook Proﬁles Harvested 
for Cambridge Analytica in Major Data Breach,” The Guardian, March 17, 2018, 
https://www.theguardian.com/news/2018/mar/17/cambridge-analytica-facebook-inﬂuence-us-electi
on. 

3.

Guy Rosen, “Security Update,” Facebook Newsroom, September 28, 2018, 
https://newsroom.fb.com/news/2018/09/security-update/.  

4.

Josh Eidelson, “Facebook Tools Are Used to Screen Out Older Job Seekers, Lawsuit Claims,” 
Bloomberg, May 29, 2018, 
https://www.bloomberg.com/news/articles/2018-05-29/facebook-tools-are-used-to-screen-out-older
-job-seekers-lawsuit-claims. 

5.

Bloomberg Editorial Board, “Think the U.S. Has a Facebook Problem? Look to Asia,” Bloomberg, 
October 22, 2017, 
https://www.bloomberg.com/opinion/articles/2017-10-22/facebook-has-a-bigger-problem-than-was
hington. 

6.

Andrew Liptak, “The US Government Alleges Facebook Enabled Housing Ad Discrimination,” The 
Verge, August 19, 2018, 
https://www.theverge.com/2018/8/19/17757108/us-department-of-housing-and-urban-development
-facebook-complaint-race-gender-discrimination. 

7.

Elizabeth Weise, “Russian Fake Accounts Showed Posts to 126 Million Facebook Users,” USA TODAY, 
October 30, 2017, 
https://www.usatoday.com/story/tech/2017/10/30/russian-fake-accounts-showed-posts-126-millio
n-facebook-users/815342001/. 

8.

Hamza Shaban, Craig Timberg, and Elizabeth Dwoskin, “Facebook, Google and Twitter Testiﬁed on 
Capitol Hill. Here’s What They Said,” Washington Post, October 31, 2017, 
https://www.washingtonpost.com/news/the-switch/wp/2017/10/31/facebook-google-and-twitter-ar
e-set-to-testify-on-capitol-hill-heres-what-to-expect/; Casey Newton, “Mark Zuckerberg’s Appearance 
before European Parliament Yields an Empty Spectacle,” The Verge, May 22, 2018, 
https://www.theverge.com/2018/5/22/17381250/mark-zuckerberg-european-parliament-facebook. 
 

 

44 

 9.

Drew Harwell, “AI will solve Facebook’s most vexing problems, Mark Zuckerberg says. Just don’t ask 
when or how,” Washington Post, April 11, 2018, 
https://www.washingtonpost.com/news/the-switch/wp/2018/04/11/ai-will-solve-facebooks-most-ve
xing-problems-mark-zuckerberg-says-just-dont-ask-when-or-how/. 

10.

Kate Conger and Dell Cameron, “Google Is Helping the Pentagon Build AI for Drones,” Gizmodo, March 
6, 2018, https://gizmodo.com/google-is-helping-the-pentagon-build-ai-for-drones-1823464533. 

11.

Rick Paulas, “A New Kind of Labor Movement in Silicon Valley,” The Atlantic, September 4, 2018, 
https://www.theatlantic.com/technology/archive/2018/09/tech-labor-movement/567808/. 

12.

Hamza Shaban, “Amazon Employees Demand Company Cut Ties with ICE,” Washington Post, June 
22, 2018, 
https://www.washingtonpost.com/news/the-switch/wp/2018/06/22/amazon-employees-demand-c
ompany-cut-ties-with-ice/; Jacob Kastrenakes, “Salesforce Employees Ask CEO to ‘Re-Examine’ 
Contract with Border Protection Agency,” The Verge, June 25, 2018, 
https://www.theverge.com/2018/6/25/17504154/salesforce-employee-letter-border-protection-ice-i
mmigration-cbp; Colin Lecher, “The Employee Letter Denouncing Microsoft’s ICE Contract Now Has 
over 300 Signatures,” The Verge, June 21, 2018, 
https://www.theverge.com/2018/6/21/17488328/microsoft-ice-employees-signatures-protest. 

13.

Nikhil Sonnad, “US Border Agents Hacked Their “Risk Assessment” System to Recommend Detention 
100% of the Time,” Quartz, June 26, 2018, 
https://qz.com/1314749/us-border-agents-hacked-their-risk-assessment-system-to-recommend-im
migrant-detention-every-time/. 

14.

Daisuke Wakabayashi, “Self-Driving Uber Car Kills Pedestrian in Arizona, Where Robots Roam,” The 
New York Times, July 30, 2018, 
https://www.nytimes.com/2018/03/19/technology/uber-driverless-fatality.html. 

15.

Nikhil Sonnad, “A Flawed Algorithm Led the UK to Deport Thousands of Students,” Quartz, May 3, 
2018, https://qz.com/1268231/a-toeic-test-led-the-uk-to-deport-thousands-of-students/. 

16.

Casey Ross and Ike Swetlitz, “IBM’s Watson Recommended ‘unsafe and Incorrect’ Cancer 
Treatments,” STAT, July 25, 2018, 
https://www.statnews.com/2018/07/25/ibm-watson-recommended-unsafe-incorrect-treatments/. 

17.

George Joseph and Kenneth Lipp, “IBM Used NYPD Surveillance Footage to Develop Technology That 
Lets Police Search by Skin Color,” The Intercept, September 6, 2018, 
https://theintercept.com/2018/09/06/nypd-surveillance-camera-skin-tone-search/. 

18.

To see a large-scale timeline of events in 2018, see: Kate Crawford and Meredith Whittaker, “AI in 
2018: A Year in Review,” Medium, October 14, 2018, 
https://medium.com/@AINowInstitute/ai-in-2018-a-year-in-review-8b161ead2b4e. 

19.

Jon Evans, “The Techlash,” TechCrunch, June 17, 2018,  

20.

“Microsoft Calls for Facial Recognition Technology Rules given ‘Potential for Abuse,’” The Guardian, 
July 14, 2018,  
https://www.theguardian.com/technology/2018/jul/14/microsoft-facial-recognition-technology-rules
-potential-for-abuse. 
 

45 

 21.

Natalie Ram, “Innovating Criminal Justice,” Northwestern University Law Review 112, no. 4 (February 
1, 2018): 659–724, https://scholarlycommons.law.northwestern.edu/nulr/vol112/iss4/2; Rebecca 
Wexler, “Life, Liberty, and Trade Secrets,” Stanford Law Review 70, no. 5 (May 2018): 1343–1429, 
https://www.stanfordlawreview.org/print/article/life-liberty-and-trade-secrets/; Danielle Keats Citron 
and Frank A. Pasquale, “The Scored Society: Due Process for Automated Predictions,” Washington 
Law Review 89, no. 1 (2014): 1–33, 
https://digital.law.washington.edu/dspace-law/bitstream/handle/1773.1/1318/89WLR0001.pdf. 

22.

See: Frank Pasquale, The Black Box Society: The Secret Algorithms That Control Money and 
Information, (Cambridge: Harvard University Press, 2015). 

23.

D. Sculley, Jasper Snoek, Alex Wiltschko, and Ali Rahimi, “Winner’s Curse? On Pace, Progress and 
Empirical Rigor,” 6th International Conference on Learning Representations (ICLR), (Vancouver, 2018), 
https://openreview.net/pdf?id=rJWF0Fywf. 

24.

Kate Crawford, “The Test We Can—and Should—Run on Facebook,” The Atlantic, July 2, 2014, 
https://www.theatlantic.com/technology/archive/2014/07/the-test-we-canand-shouldrun-on-facebo
ok/373819/; Molly Jackman and Lauri Kanerva, “Evolving the IRB: Building Robust Review for Industry 
Research,” Washington & Lee Law Review Online 72, no. 8 (June 14, 2016): 442–457; Zoltan Boka, 
“Facebook’s Research Ethics Board Needs to Stay Far Away from Facebook,” Wired, June 23, 2016, 
https://www.wired.com/2016/06/facebooks-research-ethics-board-needs-stay-far-away-facebook/. 

25.

“Sandvig v. Sessions — Challenge to CFAA Prohibition on Uncovering Racial Discrimination Online,” 
September 12, 2017, American Civil Liberties Union, 
https://www.aclu.org/cases/sandvig-v-sessions-challenge-cfaa-prohibition-uncovering-racial-discrimi
nation-online.  

26.

See: Simone Browne, Dark Matters: On the Surveillance of Blackness (Durham: Duke University Press, 
2015); Alvaro M. Bedoya, “What the FBI’s Surveillance of Martin Luther King Tells Us About the 
Modern Spy Era,” Slate, January 18, 2016, 
https://slate.com/technology/2016/01/what-the-fbis-surveillance-of-martin-luther-king-says-about-m
odern-spying.html; James Ball, Julian Borger, and Glenn Greenwald, “Revealed: How US and UK Spy 
Agencies Defeat Internet Privacy and Security,” The Guardian, September 6, 2013, 
https://www.theguardian.com/world/2013/sep/05/nsa-gchq-encryption-codes-security; Shoshana 
Zuboff, “Big Other: Surveillance Capitalism and the Prospects of an Information Civilization,” Journal 
of Information Technology 30, no. 1 (March 1, 2015): 75–89, https://doi.org/10.1057/jit.2015.5. 

27.

Alice Shen, “Facial Recognition Tech Comes to Hong Kong-Shenzhen Border,” South China Morning 
Post, July 24, 2018, 
https://www.scmp.com/news/china/society/article/2156510/china-uses-facial-recognition-system-d
eter-tax-free-traders-hong. 

28.

Stephen Chen, “China’s Robotic Spy Birds Take Surveillance to New Heights,” South China Morning 
Post, June 24, 2018, 
https://www.scmp.com/news/china/society/article/2152027/china-takes-surveillance-new-heights-ﬂ
ock-robotic-doves-do-they. 

29.

David Z. Morris, “China Will Block Travel for Those With Bad ‘Social Credit,’” Fortune, March 18, 2018, 
http://fortune.com/2018/03/18/china-travel-ban-social-credit/. 

30.

Nathan Vanderklippe, “Chinese Blacklist an Early Glimpse of Sweeping New Social-Credit Control,” 
The Globe and Mail, January 3, 2018, 
https://www.theglobeandmail.com/news/world/chinese-blacklist-an-early-glimpse-of-sweeping-newsocial-credit-control/article37493300/. 

46 

 31.

“China Has Turned Xinjiang into a Police State like No Other,” The Economist, May 31, 2018, 
https://www.economist.com/brieﬁng/2018/05/31/china-has-turned-xinjiang-into-a-police-state-like-n
o-other. 

32.

Emily Feng and Louise Lucas, “Inside China’s Surveillance State,” Financial Times, July 20, 2018, 
https://www.ft.com/content/2182eebe-8a17-11e8-bf9e-8771d5404543. 

33.

Angus Berwick, “A New Venezuelan ID, Created with China’s ZTE, Tracks Citizen Behavior,” Reuters, 
November 14, 2018, https://www.reuters.com/investigates/special-report/venezuela-zte/. 

34.

Nafeez Ahmed, “Pentagon Wants to Predict Anti-Trump Protests Using Social Media Surveillance,” 
Motherboard, October 30, 2018, 
https://motherboard.vice.com/en_us/article/7x3g4x/pentagon-wants-to-predict-anti-trump-protestsusing-social-media-surveillance. 

35.

Karen Hao, “Amazon Is the Invisible Backbone behind ICE’s Immigration Crackdown,” MIT Technology 
Review, October 22, 2018, 
https://www.technologyreview.com/s/612335/amazon-is-the-invisible-backbone-behind-ices-immigr
ation-crackdown/. 

36.

“Who’s behind Ice?” (Empower LLC, Mijente, The National Immigration Project, and the Immigrant 
Defense Project, October 23, 2018), 
https://mijente.net/wp-content/uploads/2018/10/WHO%E2%80%99S-BEHIND-ICE_-The-Tech-and-Da
ta-Companies-Fueling-Deportations_v3-.pdf. 

37.

Brendan Shillingford et al., “Large-Scale Visual Speech Recognition,” arXiv preprint [Cs], 
arXiv:1807.05162, July 13, 2018. 

38.

“Machine Vision Algorithm Learns to Recognize Hidden Facial Expressions,” MIT Technology Review, 
November 13, 2015, 
https://www.technologyreview.com/s/543501/machine-vision-algorithm-learns-to-recognize-hiddenfacial-expressions/. 

39.

Richard T. Gray, About Face: German Physiognomic Thought from Lavater to Auschwitz (Detroit: 
Wayne State University Press, 2004); Sharrona Pearl, About Faces: Physiognomy in 
Nineteenth-Century Britain (Cambridge, MA: Harvard University Press, 2010). 

40.

Blaise Aguera y Arcas, Margaret Mitchell, and Alexander Todorov, “Physiognomy’s New Clothes,” 
Medium, May 7, 2017, https://medium.com/@blaisea/physiognomys-new-clothes-f2d4b59fdd6a.  

41.

Ruth Leys, “How Did Fear Become a Scientiﬁc Object and What Kind of Object Is It?,” Representations 
110, no. 1 (2010): 66–104, https://doi.org/10.1525/rep.2010.110.1.66. Leys has offered a number of 
critiques of Ekman’s research program, most recently in Ruth Leys, The Ascent of Affect: Genealogy 
and Critique (Chicago: University of Chicago Press, 2017). 

42.

Alan J. Fridlund, Human Facial Expression: An Evolutionary View (San Diego: Academic Press, 1994). 

43.

Lisa Feldman Barrett, “Are Emotions Natural Kinds?,” Perspectives on Psychological Science 1, no. 1 
(March 2006): 28–58, https://doi.org/10.1111/j.1745-6916.2006.00003.x; Erika H. Siegel, Molly K. 
Sands, Wim Van den Noortgate, Paul Condon, Yale Chang, Jennifer Dy, Karen S. Quigley, and Lisa 
Feldman Barrett. 2018. “Emotion Fingerprints or Emotion Populations? A Meta-Analytic Investigation 
of Autonomic Features of Emotion Categories.” Psychological Bulletin 144 (4): 343–93, 
https://doi.org/10.1037/bul0000128.  
 

 

47 

 44.

For example, despite criticism by the U.S. Government Accountability Oﬃce, the Transportation 
Security Administration invested over one billion dollars in its SPOT program, aimed at identifying 
potential terrorists based on these behavioral indicators. See: “Aviation Security: TSA Should Limit 
Future Funding for Behavior Detection Activities” (Washington, DC: U.S. Government Accountability 
Oﬃce, November 13, 2013), https://www.gao.gov/products/GAO-14-159. 

45.

Jonathan Metzl, The Protest Psychosis: How Schizophrenia Became a Black Disease (Boston: Beacon 
Press, 2009). 

46.

Mark Lieberman, “Sentiment Analysis Allows Instructors to Shape Course Content around Students’ 
Emotions,” Inside Higher Education, February 20, 2018, 
https://www.insidehighered.com/digital-learning/article/2018/02/20/sentiment-analysis-allows-instr
uctors-shape-course-content. 

47.

Will Knight, “Emotional Intelligence Might Be a Virtual Assistant’s Secret Weapon,” MIT Technology 
Review, June 13, 2016, 
https://www.technologyreview.com/s/601654/amazon-working-on-making-alexa-recognize-your-em
otions; 

48.

“Affectiva Automotive AI,” Affectiva, accessed November 18, 2018, http://go.affectiva.com/auto. 

49.

Jeff Weiner, “ACLU: Amazon’s Face-Recognition Software Matched Members of Congress with 
Mugshots,” Orlando Sentinel, July 26, 2018, 
https://www.orlandosentinel.com/news/politics/political-pulse/os-amazon-rekognition-face-matchin
g-software-congress-20180726-story.html; “Amazon Rekognition Announces Real-Time Face 
Recognition, Text in Image Recognition, and Improved Face Detection,” Amazon Web Services, 
November 21, 2017, 
https://aws.amazon.com/about-aws/whats-new/2017/11/amazon-rekognition-announces-real-timeface-recognition-text-in-image-recognition-and-improved-face-detection/. 

50.

Chris Adzima, “Using Amazon Rekognition to Identify Persons of Interest for Law Enforcement,” 
Amazon Web Services, June 15, 2017, 
https://aws.amazon.com/blogs/machine-learning/using-amazon-rekognition-to-identify-persons-of-i
nterest-for-law-enforcement/. 

51.

Ranju Das, “Image & Video Rekognition Based on AWS” (Amazon Web Services Summit, Seoul, 2018), 
https://youtu.be/sUzuJc-xBEE?t=1889. 

52.

Jacob Snow, “Amazon’s Face Recognition Falsely Matched 28 Members of Congress With Mugshots,” 
American Civil Liberties Union, July 26, 2018, 
https://www.aclu.org/blog/privacy-technology/surveillance-technologies/amazons-face-recognition-f
alsely-matched-28. 

53.

Joy Buolamwini and Timnit Gebru, “Gender Shades: Intersectional Accuracy Disparities in 
Commercial Gender Classiﬁcation,” in Conference on Fairness, Accountability and Transparency (New 
York, 2018), 77–91, http://proceedings.mlr.press/v81/buolamwini18a.html. 

54.

Matt Wood, “Thoughts On Machine Learning Accuracy,” AWS News Blog, July 27, 2018, 
https://aws.amazon.com/blogs/aws/thoughts-on-machine-learning-accuracy/. 

55.

Sidney Fussell, “Amazon Accidentally Makes Rock-Solid Case for Not Giving Its Face Recognition 
Tech to Police,” Gizmodo, July 27, 2018, 
https://gizmodo.com/amazon-accidentally-makes-rock-solid-case-for-not-givin-1827934703. 

48 

 56.

Bryan Menegus, “Amazon Breaks Silence on Aiding Law Enforcement Following Employee Backlash,” 
Gizmodo, August 11, 2018, 
https://gizmodo.com/amazon-breaks-silence-on-aiding-law-enforcement-followi-1830321057. 

57.

Joseph and Lipp, “IBM Used NYPD Surveillance Footage to Develop Technology That Lets Police 
Search by Skin Color.” 

58.

Jenna Bitar and Jay Stanley, “Are Stores You Shop at Secretly Using Face Recognition on You?,” 
American Civil Liberties Union, March 26, 2018, 
https://www.aclu.org/blog/privacy-technology/surveillance-technologies/are-stores-you-shop-secretl
y-using-face. 

59.

John Brandon, “Walmart Will Scan for Unhappy Shoppers Using Facial Recognition (Cue the 
Apocalypse),” VentureBeat, August 9, 2017, 
https://venturebeat.com/2017/08/09/walmart-will-scan-for-unhappy-shoppers-using-facial-recogniti
on-cue-the-apocalypse/. 

60.

AG and Legal Workforce Act, H.R. 6417, 115th Cong. (2017-2018), 
https://www.congress.gov/115/bills/hr6417/BILLS-115hr6417ih.pdf; Securing America's Future Act 
of 2018, H.R. 4760, 115th Cong. (2017-2018), 
https://www.congress.gov/115/bills/hr4760/BILLS-115hr4760ih.pdf; Strong Visa Integrity Secures 
America Act,” H.R. 2626, 115th Cong. (2017-2018), 
https://www.congress.gov/115/bills/hr2626/BILLS-115hr2626ih.pdf; H.R. Security and Immigration 
Reform Act of 2018, 6136, 115th Cong. (2017-2018), 
https://www.congress.gov/115/bills/hr6136/BILLS-115hr6136ih.pdf.  

61.

Biometric Information Privacy Act, 740 ILCS 14/1 (Statutes current through the end of the 2018 
Regular Session of the 100th General Assembly), 
https://advance-lexis-com.proxy.library.nyu.edu/api/document?collection=statutes-legislation&id=urn
:contentItem:5C66-0WY1-6YS3-D06V-00000-00&context=1516831. 

62.

Brian Hofer, “BART Board Approves Surveillance Ordinance, Lake Merritt Development,” KTVU, 
September 13, 2018, 
http://www.ktvu.com/news/bart-board-approves-surveillance-ordinance-lake-merritt-development. 

63.

“Letter from Nationwide Coalition to Amazon CEO Jeff Bezos Regarding Rekognition,” American Civil 
Liberties Union, June 18, 2018, 
https://www.aclu.org/letter-nationwide-coalition-amazon-ceo-jeff-bezos-regarding-rekognition. 

64.

Abrar Al-Heeti, “Congress Still Wants Answers from Amazon about Its Facial Recognition Tech,” 
CNET, November 29, 2018, 
https://www.cnet.com/news/congress-still-wants-answers-from-amazon-about-its-facial-recognition
-tech/. 

65.

Woodrow Hartzog and Evan Selinger, “Facial Recognition Is the Perfect Tool for Oppression,” Medium, 
August 2, 2018, 
https://medium.com/s/story/facial-recognition-is-the-perfect-tool-for-oppression-bc2a08f0fe66. 

66.

Yilun Wang and Michael Kosinski, “Deep Neural Networks Are More Accurate than Humans at 
Detecting Sexual Orientation from Facial Images,” Journal of Personality and Social Psychology 114, 
no. 2 (2018): 246–57, http://dx.doi.org/10.1037/pspa0000098. 

67.

Frank Pasquale, “When Machine Learning Is Facially Invalid,” Communications of the ACM 61, no. 9 
(August 2018): 25–27, https://doi.org/10.1145/3241367. 

49 

 68.

Kade Crockford, “Massachusetts Should Ban Face Recognition Technology,” WBUR, August 1, 2018, 
http://www.wbur.org/cognoscenti/2018/08/01/kade-crockford-face-surveillance-technology-ban. See 
also: Hartzog and Selinger, “Facial Recognition Is the Perfect Tool for Oppression.” 

69.

Brad Smith, “Facial Recognition Technology: The Need for Public Regulation and Corporate 
Responsibility,” Microsoft on the Issues, July 13, 2018, 
https://blogs.microsoft.com/on-the-issues/2018/07/13/facial-recognition-technology-the-need-for-p
ublic-regulation-and-corporate-responsibility/; Sidney Fussell, “Axon CEO Says Face Recognition Isn’t 
Accurate Enough for Body Cams Yet,” Gizmodo, August 8, 2018, 
https://gizmodo.com/axon-ceo-says-face-recognition-isnt-accurate-enough-for-1828205723. 

70.

“Litigating Algorithms: Challenging Government Use of Algorithmic Decision Systems” (New York: AI 
Now Institute, September 2018), https://ainowinstitute.org/litigatingalgorithms.pdf; Dillon Reisman, 
Jason Schultz, Kate Crawford, and Meredith Whittaker, “Algorithmic Impact Assessments: A Practical 
Framework for Public Agency Accountability” (New York: AI Now Institute, April 2018), 
https://ainowinstitute.org/aiareport2018.pdf; Micah Altman, Alexandra Wood, and Effy Vayena, “A 
Harm-Reduction Framework for Algorithmic Fairness,” IEEE Security Privacy 16, no. 3 (May 2018): 
34–45, https://doi.org/10.1109/MSP.2018.2701149. 

71.

Lecher, “A Healthcare Algorithm Started Cutting Care, and No One Knew Why.” 

72.

Ibid. 

73.

Carlos Gradín, “World Income Inequality Database (WIID3.4),” United Nations University, October 27, 
2015, https://www.wider.unu.edu/project/wiid-world-income-inequality-database. 

74.

Serena Lei, “Nine Charts about Wealth Inequality in America,” The Urban Institute, 2015, 
http://urbn.is/wealthcharts; Lisa J. Dettling, Joanne W. Hsu, Lindsay Jacobs, Kevin B. Moore, and 
Jeffrey P. Thompson, "Recent Trends in Wealth-Holding by Race and Ethnicity: Evidence from the 
Survey of Consumer Finances," (Washington: Board of Governors of the Federal Reserve System, 
September 27, 2017), https://doi.org/10.17016/2380-7172.2083. 

75.

Andrew Restuccia, Sarah Ferris, and Helena Bottemiller Evich, “Behind Trump’s Plan to Target the 
Federal Safety Net,” POLITICO, December 11, 2017, http://politi.co/2AJfCzL. 

76.

Brakkton Booker, “White House Budget Calls For Deep Cuts To HUD,” NPR.org, February 13, 2018, 
https://www.npr.org/2018/02/13/585255697/white-house-budget-calls-for-deep-cuts-to-hud. 

77.

Alison Kodjak, “Federal Judge Blocks Medicaid Work Requirements In Kentucky,” NPR.org, June 29, 
2018, 
https://www.npr.org/sections/health-shots/2018/06/29/624807533/federal-judge-blocks-medicaidwork-requirements-in-kentucky; Rachel Garﬁeld et al., “Implications of Work Requirements in 
Medicaid: What Does the Data Say?,” The Henry J. Kaiser Family Foundation, June 12, 2018, 
https://www.kff.org/medicaid/issue-brief/implications-of-work-requirements-in-medicaid-what-doesthe-data-say/. 

78.

Stacy Dean, “President’s Budget Would Shift Substantial Costs to States and Cut Food Assistance for 
Millions,” Center on Budget and Policy Priorities, May 23, 2017, 
https://www.cbpp.org/research/food-assistance/presidents-budget-would-shift-substantial-costs-tostates-and-cut-food. 

79.

David Pegg and Niamh McIntyre, “Child Abuse Algorithms: From Science Fiction to Cost-Cutting 
Reality,” The Guardian, September 16, 2018, 
https://www.theguardian.com/society/2018/sep/16/child-abuse-algorithms-from-science-ﬁction-tocost-cutting-reality. 

50 

 80.

David Scharfenberg, “Computers Can Solve Your Problem. You May Not like the Answer,” The Boston 
Globe, September 21, 2018, https://apps.bostonglobe.com/ideas/graphics/2018/09/equity-machine; 
Joe Flood, The Fires: How a Computer Formula, Big Ideas, and the Best of Intentions Burned Down 
New York City–and Determined the Future of Cities (New York: Riverhead Books, 2011). 

81.

Virginia Eubanks, “We Created Poverty. Algorithms Won’t Make That Go Away,” The Guardian, May 13, 
2018, 
https://www.theguardian.com/commentisfree/2018/may/13/we-created-poverty-algorithms-wont-m
ake-that-go-away; 

82.

Leo Morales, “Federal Court Rules Against Idaho Department of Health and Welfare in Medicaid Class 
Action,” ACLU of Idaho, March 30, 2016, 
https://www.acluidaho.org/en/news/federal-court-rules-against-idaho-department-health-and-welfar
e-medicaid-class-action.  

83.

“Litigating Algorithms.” 

84.

Ibid., 10. 

85.

Michael Nash, “Examination of Using Structured Decision Making and Predictive Analytics in 
Assessing Safety and Risk in Child Welfare” (Los Angeles: County of Los Angeles Oﬃce of Child 
Protection, May 4, 2017), 
http://ﬁle.lacounty.gov/SDSInter/bos/bc/1023048_05.04.17OCPReportonRiskAssessmentTools_SD
MandPredictiveAnalytics_.pdf. 

86.

Virginia Eubanks, Automating Inequality: How High-Tech Tools Proﬁle, Police, and Punish the Poor 
(New York: St. Martin’s Press, 2018). 

87.

“Vulnerable Children Predictive Modelling” (Wellington: New Zealand Ministry of Social Development), 
accessed November 18, 2018, 
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elling/. 

88.

Eubanks, Automating Inequality. 

89.

Margaret Talbot, “The Case Against Cash Bail,” The New Yorker, August 25, 2015, 
https://www.newyorker.com/news/news-desk/the-case-against-cash-bail. 

90.

Sam Levin, “Imprisoned by Algorithms: The Dark Side of California Ending Cash Bail,” The Guardian, 
September 7, 2018, 
https://www.theguardian.com/us-news/2018/sep/07/imprisoned-by-algorithms-the-dark-side-of-cali
fornia-ending-cash-bail; Maddie Hanna, “What Happened When New Jersey Stopped Relying on Cash 
Bail,” The Philadelphia Inquirer, February 16, 2018, 
http://www2.philly.com/philly/news/new_jersey/new-jersey-cash-bail-risk-assessment-20180216.ht
ml. 

91.

Colleen O’Dea, “Civil Rights Coalition Calls for End to Core Element of NJ Bail Reform,” NJ Spotlight, 
July 31, 2018, 
http://www.njspotlight.com/stories/18/07/30/civil-rights-groups-call-for-end-to-core-element-of-nj-ba
il-reform/.  

92.

Jeremy B. White, “California Ended Cash Bail. Why Are So Many Reformers Unhappy About It?,” 
POLITICO Magazine, August 29, 2018, https://politi.co/2PPLnyz. 

51 

 93.

93. Rose Luckin, “Towards Artiﬁcial Intelligence-Based Assessment Systems,” Nature Human 
Behaviour 1 (March 1, 2017): 1–3, https://doi.org/10.1038/s41562-016-0028. 

94.

Brent Bridgeman, Catherine Trapani, and Yigal Attali, “Comparison of Human and Machine Scoring of 
Essays: Differences by Gender, Ethnicity, and Country,” Applied Measurement in Education 25, no. 1 
(January 2012): 27–40, https://doi.org/10.1080/08957347.2012.635502. 

95.

Nitin Madnani et al., “Building Better Open-Source Tools to Support Fairness in Automated Scoring,” in 
Proceedings of the First ACL Workshop on Ethics in Natural Language Processing (Valencia: 
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96.

Daniel T. O’Brien et al., “An Evaluation of Equity in the Boston Public Schools’ Home-Based 
Assignment Policy” (Boston: Boston Area Research Initiative, July 2018), 
https://news.northeastern.edu/wp-content/uploads/2018/07/BPSHBAP.pdf. 

97.

Ibid. 

98.

Scharfenberg, “Computers Can Solve Your Problem. You May Not like the Answer.”  

99.

Ibid. 

100. Dillon Reisman, Jason Schultz, Kate Crawford, and Meredith Whittaker, “Algorithmic Impact 
Assessments: A Practical Framework for Public Agency Accountability” (New York: AI Now Institute, 
April 2018), https://ainowinstitute.org/aiareport2018.pdf. 
101. “Litigating Algorithms.” 
102. “Algorithmic Accountability Policy Toolkit” (New York: AI Now Institute, October 2018), 
https://ainowinstitute.org/aap-toolkit.pdf.  
103. Reisman et al., “Algorithmic Impact Assessments.” 
104. Wakabayashi, “Self-Driving Uber Car Kills Pedestrian in Arizona.” 
105. Jack Stewart, “Tesla’s Self-Driving Autopilot Involved in Another Deadly Crash,” Wired, March 31, 2018, 
https://www.wired.com/story/tesla-autopilot-self-driving-crash-california/. 
106. “Uber Autonomous-SUV Driver Streamed ‘The Voice’ Just before Deadly Arizona Crash, Report Says,” 
Los Angeles Times, June 22, 2018, 
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107. “Early Rider Program,” Waymo, accessed November 16, 2018, https://waymo.com/apply/. 
108. Aarian Marshall, “Wanna Save Lots of Lives? Put (Imperfect) Self-Driving Cars on the Road, ASAP,” 
Wired, November 7, 2017, https://www.wired.com/story/self-driving-cars-rand-report/. 
109. Lindsay Moore, “Autonomous Vehicles Continue to Drive Without Rules,” Phoenix New Times, 
November 9, 2017, 
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3743. 
110. “Uber Autonomous-SUV Driver Streamed ‘The Voice’ Just before Deadly Arizona Crash, Report Says.”  
111. Ross and Swetlitz, “IBM’s Watson Recommended ‘unsafe and Incorrect’ Cancer Treatments.”  
 

 

52 

 112. Angela Chen, “What the Apple Watch’s FDA Clearance Actually Means,” The Verge, September 13, 
2018, 
https://www.theverge.com/2018/9/13/17855006/apple-watch-series-4-ekg-fda-approved-vs-clearedmeaning-safe. 
113. Mark Vermette, “Apple Watch Approval Marks Shift In Device Development And Approvals,” Med 
Device Online, October 24, 2018, 
https://www.meddeviceonline.com/doc/apple-watch-approval-marks-shift-in-device-development-an
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Healthcare Informatics Magazine, November 7, 2018, 
https://www.healthcare-informatics.com/news-item/cybersecurity/44m-patient-records-breached-q3
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114. Alex Hern, “Google ‘betrays Patient Trust’ with DeepMind Health Move,” The Guardian, November 14, 
2018, 
https://www.theguardian.com/technology/2018/nov/14/google-betrays-patient-trust-deepmind-healt
hcare-move. 
115. Benjamin Herold, “Pearson Tested ‘Social-Psychological’ Messages in Learning Software, With Mixed 
Results,” Education Week, April 17, 2018, 
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116. Rolfe Winkler and Laura Stevens, “New Parents Complain Amazon Baby-Registry Ads Are Deceptive,” 
Wall Street Journal, November 28, 2018, 
https://www.wsj.com/articles/new-parents-complain-amazon-ads-are-deceptive-1543417201. 
117. Julia Angwin, Jeff Larson, and Laura Kirchner, “Machine Bias: There’s Software Used Across the 
Country to Predict Future Criminals. And It’s Biased Against Blacks.,” ProPublica, May 23, 2016, 
https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing; Jeffrey 
Dastin, “Amazon Scraps Secret AI Recruiting Tool That Showed Bias against Women,” Reuters, 
October 10, 2018, 
https://www.reuters.com/article/us-amazon-com-jobs-automation-insight-idUSKCN1MK08G. 
118. Shira Mitchell, “Mirror Mirror: Reﬂections on Quantitative Fairness,” 2018, 
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119. Solon Barocas, Kate Crawford, Aaron Shapiro, Hanna Wallach, 2017, “The Problem with Bias: 
Allocative Versus Representational Harms in Machine Learning” (Measure, Model, Mix: Computer as 
Instrument: 9th Annual SIGCIS Conference, Philadelphia, 2017). 
120. Kate Crawford, “The Trouble with Bias” (Conference on Neural Information Processing Systems, Long 
Beach, CA, 2017), https://www.youtube.com/watch?v=fMym_BKWQzk. 
121. Ibid. 
122. Sam Corbett-Davies and Sharad Goel, “The Measure and Mismeasure of Fairness: A Critical Review of 
Fair Machine Learning,” arXiv preprint [CS] arXiv:1808.00023, July 31, 2018. 
123. Solon Barocas and Moritz Hardt, “Fairness in Machine Learning” (Conference on Neural Information 
Processing Systems, Long Beach, CA, 2017), https://mrtz.org/nips17/#/; Narayanan, 21 Fairness 
Deﬁnitions and Their Politics. 
 

 

53 

 124. Tracy Jan, “Redlining Was Banned 50 Years Ago. It’s Still Hurting Minorities Today,” Washington Post, 
March 28, 2018, 
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ts-still-hurting-minorities-today/. 
125. Cynthia Dwork, Nicole Immorlica, Adam T. Kalai, Mark DM Leiserson, “Decoupled classiﬁers for 
group-fair and eﬃcient machine learning”, Conference on Fairness, Accountability and Transparency 
(January 21, 2018), 119-133, http://proceedings.mlr.press/v81/dwork18a/dwork18a.pdf.  
126. Jon Kleinberg, “Inherent Trade-Offs in Algorithmic Fairness,” in Abstracts of the 2018 ACM 
International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS ’18 (New 
York: ACM, 2018), 40–40, https://doi.org/10.1145/3219617.3219634; Alexandra Chouldechova, “Fair 
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[Cs, Stat] arXiv:1610.07524, October 24, 2016. 
127. Frank Pasquale, “Odd Numbers,” Real Life, August 20, 2018, https://reallifemag.com/odd-numbers/. 
128. Sebastian Benthall, “Critical Reﬂections on FAT* 2018: A Historical Idealist Perspective,” Dataactive, 
April 11, 2018, 
https://data-activism.net/2018/04/critical-reﬂections-on-fat-2018-a-historical-idealist-perspective/. 
129. Mitchell, “Mirror Mirror: Reﬂections on Quantitative Fairness.” 
130. Ben Hutchinson and Margaret Mitchell, “50 Years of Test (Un)Fairness: Lessons for Machine 
Learning,” arXiv preprint [CS], arXiv:1811.10104, November 25, 2018. 
131. Roel Dobbe, Sarah Dean, Thomas Gilbert, and Nitin Kohli, “A Broader View on Bias in Automated 
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arXiv:1807.00553, July 2, 2018. 
132. Batya Friedman and Helen Nissenbaum, “Discerning Bias in Computer Systems,” in INTERACT ’93 and 
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141–142, https://doi.org/10.1145/259964.260152. 
133. Batya Friedman and Helen Nissenbaum, “Bias in Computer Systems,” ACM Transactions on 
Information Systems 14, no. 3 (July 1996): 330–347, https://doi.org/10.1145/230538.230561. 
134. Kate Crawford and Ryan Calo, “There Is a Blind Spot in AI Research,” Nature 538, no. 7625 (October 
20, 2016): 311, https://doi.org/10.1038/538311a. 
135. See: Meredith Whittaker, “Data Genesis: AI’s Primordial Soup” (Eyeo Festival, Minneapolis, 2018), 
https://vimeo.com/287094149. 
136. Timnit Gebru et al., “Datasheets for Datasets,” arXiv preprint [Cs], arXiv:1803.09010, March 23, 2018. 
137. Margaret Mitchell et al., “Model Cards for Model Reporting,” arXiv preprint [Cs] arXiv:1810.03993, 
October 5, 2018. 
138. Jevan A. Hutson, Jessie G. Taft, Solon Barocas and Karen Levy,, “Debiasing Desire: Addressing Bias & 
Discrimination on Intimate Platforms,” Proceedings of the ACM On Human-Computer Interaction 
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139. Animesh Singh and Michael Hind, “AI Fairness 360: Attacking Bias from All Angles!,” IBM Developer, 
September 19, 2018, 
https://developer.ibm.com/blogs/2018/09/19/ai-fairness-360-attacking-bias-from-all-angles/. 

54 

 140. James Wexler, “The What-If Tool: Code-Free Probing of Machine Learning Models,” Google AI Blog, 
September 11, 2018, http://ai.googleblog.com/2018/09/the-what-if-tool-code-free-probing-of.html; 
James Wexler, “Facets: An Open Source Visualization Tool for Machine Learning Training Data,” 
Google AI Blog, June 17, 2017, 
http://ai.googleblog.com/2017/07/facets-open-source-visualization-tool.html. 
141. Alekh Agarwal et al., “A Reductions Approach to Fair Classiﬁcation,” arXiv preprints [CS], 
arXiv:1803.02453, March 6, 2018. 
142. Dave Gershgorn, “Facebook Says It Has a Tool to Detect Bias in Its Artiﬁcial Intelligence,” Quartz, May 
3, 2018, 
https://qz.com/1268520/facebook-says-it-has-a-tool-to-detect-bias-in-its-artiﬁcial-intelligence/. 
143. Jeremy Kahn, “Accenture Unveils Tool to Help Companies Insure Their AI Is Fair,” Bloomberg, June 13, 
2018, 
https://www.bloomberg.com/news/articles/2018-06-13/accenture-unveils-tool-to-help-companies-in
sure-their-ai-is-fair. 
144. “The Ethics Certiﬁcation Program for Autonomous and Intelligent Systems (ECPAIS),” IEEE Standards 
Association, accessed November 19, 2018, 
https://standards.ieee.org/industry-connections/ecpais.html. 
145. “TRUSTe Settles FTC Charges It Deceived Consumers Through Its Privacy Seal Program,” Federal 
Trade Commission, November 17, 2014, 
https://www.ftc.gov/news-events/press-releases/2014/11/truste-settles-ftc-charges-it-deceived-con
sumers-through-its. 
146. “AI Now 2017 Report.”  
147. Scott Shane, Cade Metz, and Daisuke Wakabayashi, “How a Pentagon Contract Became an Identity 
Crisis for Google,” The New York Times, November 2, 2018, 
https://www.nytimes.com/2018/05/30/technology/google-project-maven-pentagon.html. 
148. Sundar Pichai, “AI at Google: Our Principles,” The Keyword, June 7, 2018, 
https://www.blog.google/technology/ai/ai-principles/. 
149. Alan Boyle, “Microsoft Is Turning down Some Sales over AI Ethics, Top Researcher Eric Horvitz Says,” 
GeekWire, April 10, 2018, 
https://www.geekwire.com/2018/microsoft-cutting-off-sales-ai-ethics-top-researcher-eric-horvitz-say
s/; Jordan Novet, “Facebook Forms Ethics Team to Prevent Bias in AI Software,” CNBC, May 3, 2018, 
https://www.cnbc.com/2018/05/03/facebook-ethics-team-prevents-bias-in-ai-software.html; “Axon 
AI and Policing Technology Ethics Board,” Axon, accessed November 19, 2018, 
https://www.axon.com/info/ai-ethics. 
150. “Ethically Aligned Design Version 2: A Vision for Prioritizing Human Well-Being with Autonomous and 
Intelligent Systems” (New York: IEEE Global Initiative on Ethics of Autonomous and Intelligent 
Systems, 2018), https://ethicsinaction.ieee.org/; Brent Hecht, Lauren Wilcox, Jeffrey P. Bigham, 
Johannes Schöning, Ehsan Hoque, Jason Ernst, Yonatan Bisk, Luigi De Russis, Lana Yarosh, Bushra 
Anjum, Danish Contractor, and Cathy Wu. “It’s Time to Do Something: Mitigating the Negative Impacts 
of Computing Through a Change to the Peer Review Process,” ACM Future of Computing Blog, March 
29, 2018, https://acm-fca.org/2018/03/29/negativeimpacts/. 
151. Alina Tugend, “Colleges Grapple With Teaching the Technology and Ethics of A.I.,” The New York 
Times, November 3, 2018, 
https://www.nytimes.com/2018/11/02/education/learning/colleges-grapple-with-teaching-ai.html. 

55 

 152. “La Déclaration de Montréal pour un Développement Responsable de l’Intelligence Artiﬁcielle,” 
accessed November 19, 2018, https://www.declarationmontreal-iaresponsable.com/. 
153. “Microsoft Professional Program for Artiﬁcial Intelligence,” Microsoft, accessed November 29, 2018, 
https://academy.microsoft.com/en-us/professional-program/tracks/artiﬁcial-intelligence/. 
154. Lucy Suchman, “Corporate Accountability,” Robot Futures, June 11, 2018, 
https://robotfutures.wordpress.com/2018/06/10/corporate-accountability/.  
155. Ben Wagner, “Ethics as Escape From Regulation: From Ethics-Washing to Ethics-Shopping?” In Being 
Proﬁling. Cogitas Ergo Sum, ed. Mireille Hildebrandt. (Amsterdam: Amsterdam University Press, 
forthcoming 2019), 
https://www.privacylab.at/wp-content/uploads/2018/07/Ben_Wagner_Ethics-as-an-Escape-from-Re
gulation_2018_BW9.pdf. 
156. Sandy Parakilas, “We Can’t Trust Facebook to Regulate Itself,” The New York Times, January 20, 2018. 
https://www.nytimes.com/2017/11/19/opinion/facebook-regulation-incentive.html.  
157. Dan M. Greene, Anna Laura Hoffman, and Luke Stark, “Better, Nicer, Clearer, Fairer: A Critical 
Assessment of the Movement for Ethical Artiﬁcial Intelligence and Machine Learning” (Hawaii 
International Conference on System Sciences, Maui, forthcoming 2019), 
http://dmgreene.net/wp-content/uploads/2018/09/Greene-Hoffman-Stark-Better-Nicer-Clearer-Fairer
-HICSS-Final-Submission.pdf.  
158. Ibid. 
159. Andrew McNamara, Justin Smith, and Emerson Murphy-Hill, “Does ACM’s Code of Ethics Change 
Ethical Decision Making in Software Development?,” in Proceedings of the 2018 26th ACM Joint 
Meeting on European Software Engineering Conference and Symposium on the Foundations of 
Software Engineering, ESEC/FSE 2018 (New York: ACM, 2018), 729, 
https://doi.org/10.1145/3236024.3264833. 
160. Pichai, “AI at Google: Our Principles.” 
161. Ryan Gallagher, “Google Plans to Launch Censored Search Engine in China, Leaked Documents 
Reveal,” The Intercept, August 1, 2018, 
https://theintercept.com/2018/08/01/google-china-search-engine-censorship/. 
162. Ronald Deibert, Rebecca Mackinnon, Xiao Qiang, and Lokman Tsui, “Open Letter to Google on 
Reported Plans to Launch a Censored Search Engine in China,” Amnesty International, August 28, 
2018, 
https://www.amnesty.org/en/documents/document/?indexNumber=ASA17%2f9001%2f2018&langu
age=en; Kate Conger and Daisuke Wakabayashi, “Google Employees Protest Secret Work on 
Censored Search Engine for China,” The New York Times, September 10, 2018, 
https://www.nytimes.com/2018/08/16/technology/google-employees-protest-search-censored-chin
a.html. 
163. Mark Bergen, “Google CEO Tells Staff China Plans Are ‘Exploratory’ After Backlash,” Bloomberg August 
17, 2018, 
https://www.bloomberg.com/news/articles/2018-08-17/google-ceo-is-said-to-tell-staff-china-plans-a
re-exploratory. 
 

 

56 

 164. Matt Phillips, “Facebook’s Stock Plunge Shatters Faith in Tech Companies’ Invincibility,” The New York 
Times, October 17, 2018, 
https://www.nytimes.com/2018/07/26/business/facebook-stock-earnings-call.html; Rupert Neate, 
“Twitter Stock Plunges 20% in Wake of 1m User Decline,” The Guardian, July 27, 2018, 
https://www.theguardian.com/technology/2018/jul/27/twitter-share-price-tumbles-after-it-loses-1musers-in-three-months. 
165. For a more general description of justice as fairness, see: John Rawls, Justice as Fairness: A 
Restatement, ed. Erin I. Kelly (Cambridge, MA: Harvard University Press, 2001). 
166. Brent Hecht et al., “It’s Time to Do Something,” https://acm-fca.org/2018/03/29/negativeimpacts/. 
167. Ben Green, “‘Fair’ Risk Assessments: A Precarious Approach for Criminal Justice Reform” (5th 
Workshop on Fairness, Accountability, and Transparency in Machine Learning, Stockholm, 2018), 
https://scholar.harvard.edu/ﬁles/bgreen/ﬁles/18-fatml.pdf. 
168. Ben Green, “Putting the J(ustice) in FAT,” Berkman Klein Center, February 26, 2018, 
https://medium.com/berkman-klein-center/putting-the-j-ustice-in-fat-28da2b8eae6d. 
169. Kate Crawford, “Just An Engineer: The Politics of AI,” (The Royal Society, London, July 2018), 
https://www.youtube.com/watch?v=HPopJb5aDyA. 
170. Paul N. Edwards, Geoffrey C. Bowker, Steven J. Jackson, and Robin Williams. “Introduction: An 
Agenda for Infrastructure Studies,” Journal of the Association for Information Systems 10, no. 5 (May 
28, 2009): 364–74, https://aisel.aisnet.org/jais/vol10/iss5/6. 
171. Susan Leigh Star, “The Ethnography of Infrastructure,” American Behavioral Scientist 43, no. 3 
(November 1, 1999): 377–91, https://doi.org/10.1177/00027649921955326; Paul N. Edwards, 
Thomas J. Misa, and Philip Brey, “Infrastructure and Modernity: Force, Time, and Social Organization 
in the History of Sociotechnical Systems,” in Modernity and Technology (Cambridge, MA: MIT Press, 
2003), 185–225; Shannon Mattern, “The Big Data of Ice, Rocks, Soils, and Sediments”, Places, 
November 2017, https://placesjournal.org/article/the-big-data-of-ice-rocks-soils-and-sediments/; 
Jean-Christophe Plantin, Carl Lagoze, Paul N. Edwards and Christian Sandvig. “Infrastructure studies 
meet platform studies in the age of Google and Facebook,” New Media & Society, 20, no. 1 (2018): 
293-310, https://doi.org/10.1177/1461444816661553. 
172. Kate Crawford and Vladan Joler, “Anatomy of an AI System: The Amazon Echo As An Anatomical 
Map of Human Labor, Data and Planetary Resources,” (AI Now Institute and Share Lab, September 7, 
2018), https://anatomyof.ai. 
173. Steven J. Jackson, “Rethinking Repair”, in Media Technologies: Essays on Communication, Materiality, 
and Society, Tarleton Gillespie, Pablo J. Boczkowski and Kirsten A. Foot, eds. (Cambridge: MIT Press, 
2014), 221-239. 
174. Nick Seaver, “What Should An Anthropology of Algorithms Do?” Cultural Anthropology, 33, No. 3 
(2018): 375-385, https://doi.org/10.14506/ca33.3.04.  
175. Li Yuan, “How Cheap Labor Drives China’s A.I. Ambitions,” The New York Times, November 27, 2018, 
https://www.nytimes.com/2018/11/25/business/china-artiﬁcial-intelligence-labeling.html; Mary L. 
Gray and Siddharth Suri, “The Humans Working Behind the AI Curtain, Harvard Business Review, 
January 9, 2017, https://hbr.org/2017/01/the-humans-working-behind-the-ai-curtain.  
176. Lilly Irani, “The Hidden Faces of Automation,” XRDS 23, no. 2 (December 2016): 34–37, 
https://doi.org/10.1145/3014390.  

57 

 177. Ibid. 
178. Janine Berg et al., “Digital Labour Platforms and the Future of Work: Towards Decent Work in the 
Online World,” (Geneva: International Labour Organization, September 20, 2018), 
http://www.ilo.org/global/publications/books/WCMS_645337/lang--en/index.htm. 
179. Sarah T. Roberts, “Commercial Content Moderation: Digital Laborers’ Dirty Work,” in The Intersectional 
Internet: Race, Sex, Class and Culture Online, ed. Saﬁya Umoja Noble and Brendesha M. Tynes (New 
York: Peter Lang, 2016), 147–159. 
180. M. S. Silberman et al., “Responsible Research with Crowds: Pay Crowdworkers at Least Minimum 
Wage,” Communications of the ACM 61, no. 3 (February 2018): 39–41, 
https://doi.org/10.1145/3180492. 
181. Arlene Kaplan Daniels, “Invisible Work,” Social Problems 34, no. 5 (1987): 403–15, 
https://doi.org/10.2307/800538; Arlie Russell Hochschild, The Managed Heart: Commercialization of 
Human Feeling (Berkeley: University of California Press, 2012). 
182. Astra Taylor, “The Automation Charade,” Logic Magazine, October 2, 2018, 
https://logicmag.io/05-the-automation-charade/. 
183. Jana Kasperkevic, “Ex-McDonald’s CEO Suggests Replacing Employees with Robots amid Protests,” 
The Guardian, May 25, 2016, 
https://www.theguardian.com/us-news/2016/may/25/former-mcdonalds-ceo-threatens-replace-em
ployees-robots; Ed Rensi, “Thanks To ‘Fight For $15’ Minimum Wage, McDonald’s Unveils 
Job-Replacing Self-Service Kiosks Nationwide,” Forbes, November 29, 2016, 
https://www.forbes.com/sites/realspin/2016/11/29/thanks-to-ﬁght-for-15-minimum-wage-mcdonal
ds-unveils-job-replacing-self-service-kiosks-nationwide/#5defa3eb4fbc. 
184. Uliana Pavlova, “McDonald’s Kiosks Mean More Staff at Chicago Flagship, Not Fewer,” Bloomberg, 
August 8, 2018, 
https://www.bloomberg.com/news/articles/2018-08-08/mcdonald-s-kiosks-mean-more-staff-at-chic
ago-ﬂagship-not-fewer. 
185. Alex Rosenblat, Uberland: How Algorithms Are Rewriting the Rules of Work (Berkeley: University of 
California Press, 2018). 
186. Ben Chapman, “Uber Eats and Deliveroo Riders Are Going on Strike This Week,” The Independent, 
October 3, 2018, 
https://www.independent.co.uk/news/business/news/uber-eats-deliveroo-strike-mcdonalds-wethers
poons-mcstrike-industrial-action-a8567286.html; Carolyn Said, “Uber, Lyft Drivers Fear Getting Booted 
from Work,” San Francisco Chronicle, October 14, 2018, 
https://www.sfchronicle.com/business/article/Uber-Lyft-drivers-fear-getting-booted-from-work-1330
4052.php. 
187. Kate Conger, “Google and Facebook’s Security Guards Are Fighting to Earn a Living Wage,” Gizmodo, 
July 27, 2018, 
https://gizmodo.com/google-and-facebooks-security-guards-are-ﬁghting-to-ea-1826104897. 
188. Mark Bergen and Josh Eidelson, “Inside Google’s Shadow Workforce,” Bloomberg, July 25, 2018, 
https://www.bloomberg.com/news/articles/2018-07-25/inside-google-s-shadow-workforce. 
 

 

58 

 189. Thuy Ong, “Amazon Patents Wristbands That Track Warehouse Employees’ Hands in Real Time,” The 
Verge, February 1, 2018, 
https://www.theverge.com/2018/2/1/16958918/amazon-patents-trackable-wristband-warehouse-e
mployees; Hayley Peterson, “Missing Wages, Grueling Shifts, and Bottles of Urine: The Disturbing 
Accounts of Amazon Delivery Drivers May Reveal the True Human Cost of ‘Free’ Shipping,” Business 
Insider, September 11, 2018, 
https://www.businessinsider.com/amazon-delivery-drivers-reveal-claims-of-disturbing-work-conditio
ns-2018-8. 
190. Catie Keck, “Amazon Workers Across Europe Protest Black Friday, Citing Grueling Work Conditions,” 
Gizmodo, November 23, 2018, 
https://gizmodo.com/amazon-workers-across-europe-protest-black-friday-citi-1830622250. 
191. “AI Now 2017 Report.”  
192. L. Rafael Reif, “Letter to the MIT Community Regarding the MIT Stephen A. Schwarzman College of 
Computing,” MIT News, October 15, 2018, 
https://news.mit.edu/2018/letter-mit-community-regarding-mit-stephen-schwarzman-college-compu
ting; Steve Lohr, “M.I.T. Plans College for Artiﬁcial Intelligence, Backed by $1 Billion,” The New York 
Times, October 16, 2018, 
https://www.nytimes.com/2018/10/15/technology/mit-college-artiﬁcial-intelligence.html. 
193. Lev Manovich, “Can We Think Without Categories?” Digital Culture & Society, 4, no. 1 (2018): 17-28.  
194. AI Now Institute, “Gender, Race and Power: Outlining a New AI Research Agenda,” Medium, November 
15, 2018. https://medium.com/@AINowInstitute/gender-race-and-power-5da81dc14b1b. 
195. Kristian Lum, “Statistics, we have a problem,” Medium, December 13, 2017, 
https://medium.com/@kristianlum/statistics-we-have-a-problem-304638dc5de5.  
196. Jennings Brown, “‘NIPS’ AI Conference Changes Name Following Protests Over Gross Acronym,” 
Gizmodo, November 19, 2018. 
https://gizmodo.com/nips-ai-conference-changes-name-following-protests-ov-1830548185; Corinna 
Cortes et al., “From the Board: Changing Our Acronym,” NeurIPS November 16, 2018, 
https://nips.cc/Conferences/2018/News.  
197. “NIPS Name Change,” NeurIPS, October 17, 2018, https://nips.cc/Conferences/2018/News.  
198. Meredith Whittaker, one of the co-founders of AI Now, was one of the organizers of the Google 
Walkout. See also: Caroline O’Donovan and Ryan Mac, “Google Engineers Are Organizing A Walkout 
To Protest The Company’s Protection Of An Alleged Sexual Harasser”, Buzzfeed, Oct. 30, 2018, 
https://www.buzzfeednews.com/article/carolineodonovan/googles-female-engineers-walkout-sexual
-harassment. 
199. Davey Alba and Caroline O’Donovan, “Square, Airbnb, And eBay Just Said They Would End Forced 
Arbitration For Sexual Harassment Claims,” Buzzfeed, Nov. 15, 2018, 
https://www.buzzfeednews.com/article/daveyalba/tech-companies-end-forced-arbitration-airbnb-eb
ay. 
200. Sharon Florentine, “Alphabet Dismisses Action on Diversity and Inclusion,” CIO, June 15, 2018, 
https://www.cio.com/article/3281867/it-industry/alphabet-dismisses-action-on-diversity-and-inclusio
n.html; Sara Ashley O’Brien, “Apple’s Board Calls Diversity Proposal ‘Unduly Burdensome and Not 
Necessary,’” CNNMoney, January 15, 2016, 
https://money.cnn.com/2016/01/15/technology/apple-diversity/index.html. 

59 

 201. Tom Simonite, “AI Is the Future - But Where Are the Women?,” Wired, August 17, 2018, 
https://www.wired.com/story/artiﬁcial-intelligence-researchers-gender-imbalance/. 
202. Jackie Snow, “‘We’re in a Diversity Crisis:’ Cofounder of Black in AI on What’s Poisoning Algorithms in 
Our Lives,” MIT Technology Review, February 14, 2018, 
https://www.technologyreview.com/s/610192/were-in-a-diversity-crisis-black-in-ais-founder-on-what
s-poisoning-the-algorithms-in-our/. 
203. Paula A. Johnson, Sheila E. Widnall, and Frazier F. Benya, eds., Sexual Harassment of Women: Climate, 
Culture, and Consequences in Academic Sciences, Engineering, and Medicine (Washington, DC: The 
National Academies Press, 2018), 
https://www.nap.edu/catalog/24994/sexual-harassment-of-women-climate-culture-and-consequenc
es-in-academic. 
204. Caroline Clark Hayes, “Computer Science: The Incredible Shrinking Woman,” in Gender Codes: Why 
Women Are Leaving Computing, ed. Thomas J Misa (Hoboken: Wiley, 2010), 25–50. 
205. Dale Strok, “Women in AI,” IEEE Expert: Intelligent Systems and Their Applications 7, no. 4 (August 
1992): 7–22, https://doi.org/10.1109/64.153460. 
206. “Barriers to Equality in Academia: Women in Computer Science at M.I.T.” (Cambridge, MA: The 
Laboratory of Computer Science and the Artiﬁcial Intelligence Laboratory at M.I.T., February 1983), 
https://homes.cs.washington.edu/~lazowska/mit/Images/title.pdf. 
207. Langdon Winner, “Do Artifacts Have Politics?,” Daedalus 109, no. 1 (1980): 121–36. 
208. Stephen Merity, “Bias is not just in our datasets, it’s in our conferences and community,” Smerity.com, 
December 11, 2017, https://smerity.com/articles/2017/bias_not_just_in_datasets.html. 
209. Dastin, “Amazon scraps secret AI recruiting tool that showed bias against women.”  
210. Saﬁya U. Noble, Algorithms of Oppression: How Search Engines Reinforce Racism, (New York: NYU 
Press, 2018);  
211. Clemens Apprich, Wendy Hui Kyong Chun, Florian Cramer, and Hito Steyerl, Pattern Discrimination 
(Minneapolis: University of Minnesota Press, forthcoming 2019).  
212. “Litigating Algorithms.” 
213. Reisman et al., “Algorithmic Impact Assessments.” 
214. “CPSR History,” Computer Professionals for Social Responsibility, June 1, 2005, 
http://cpsr.org/about/history/. 
215. “The Never Again Pledge,” accessed November 29, 2018, http://neveragain.tech/ 
216. Scott Shane and Daisuke Wakabayashi, “‘The Business of War:’ Google Employees Protest Work for 
the Pentagon,” The New York Times, November 2, 2018, 
https://www.nytimes.com/2018/04/04/technology/google-letter-ceo-pentagon-project.html. 
 

 

60 

 217. Lucy Suchman, Lilly Irani, and Peter Asaro, “Google’s March to the Business of War Must Be Stopped,” 
The Guardian, May 16, 2018, 
https://www.theguardian.com/commentisfree/2018/may/16/google-business-war-project-maven; 
Mary Wareham, “Letter to Sergey Brin and Sundar Pichai,” March 13, 2018, 
https://www.stopkillerrobots.org/wp-content/uploads/2018/04/KRC_LtrGoogle_12March2018.pdf; 
Daisuke Wakabayashi and Scott Shane, “Google Will Not Renew Pentagon Contract That Upset 
Employees,” The New York Times, November 2, 2018, 
https://www.nytimes.com/2018/06/01/technology/google-pentagon-project-maven.html. 
218. Shaban, “Amazon Employees Demand Company Cut Ties with ICE;” “Who’s Behind Ice?;” Caroline 
O’Donovan, “Employees Of Another Major Tech Company Are Petitioning Government Contracts,” 
BuzzFeed News, June 26, 2018, 
https://www.buzzfeednews.com/article/carolineodonovan/salesforce-employees-push-back-against
-company-contract; Sheera Frenkel, “Microsoft Employees Question C.E.O. Over Company’s Contract 
With ICE,” The New York Times, July 27, 2018, 
https://www.nytimes.com/2018/07/26/technology/microsoft-ice-immigration.html; Peter Kotecki, 
“Burning Man Protesters Raise Awareness of Palantir, Amazon ICE Ties,” Business Insider, August 31, 
2018, https://www.businessinsider.com/burning-man-protestors-palantir-amazon-ice-2018-8. 
219. Greg Sandoval, “Over 100 Amazon Employees Sign Letter Asking Jeff Bezos to Stop Selling 
Facial-Recognition Software to Police,” Business Insider, June 22, 2018, 
https://www.businessinsider.com/over-100-amazon-employees-sign-letter-jeff-bezos-stop-selling-fac
ial-recognition-software-police-2018-6; Kade Crockford, “Over 150,000 People Tell Amazon: Stop 
Selling Facial Recognition Tech to Police,” American Civil Liberties Union, June 18, 2018, 
https://www.aclu.org/blog/privacy-technology/surveillance-technologies/over-150000-people-tell-am
azon-stop-selling-facial; Matt Cagle and Nicole Ozer, “Amazon Teams Up With Government to Deploy 
Dangerous New Facial Recognition Technology,” ACLU Free Future, May 22, 2018, 
https://www.aclu.org/blog/privacy-technology/surveillance-technologies/amazon-teams-governmen
t-deploy-dangerous-new. 
220. Ryan Gallagher “Google China Prototype Links Searches to Phone Numbers” The Intercept, 
September 14, 2018, 
https://theintercept.com/2018/09/14/google-china-prototype-links-searches-to-phone-numbers/. 
221. Bryan Menegus, “Here’s the Letter 1,400 Google Workers Sent Leadership in Protest of Censored 
Search Engine for China,” Gizmodo, August 16, 2018, 
https://gizmodo.com/heres-the-letter-1-400-google-workers-sent-leadership-i-1828393599; Google 
Employees Against Dragonﬂy, “We Are Google Employees. Google Must Drop Dragonﬂy.,” Medium, 
November 27, 2018, 
https://medium.com/@googlersagainstdragonﬂy/we-are-google-employees-google-must-drop-drago
nﬂy-4c8a30c5e5eb; “Google Must Not Capitulate to China’s Censorship Demands,” Amnesty 
International, November 28, 2018, 
https://www.amnesty.org/en/latest/news/2018/11/google-must-not-capitulate-to-chinas-censorship
-demands/. 
222. Co-founder of AI Now, Meredith Whittaker, was one of the eight core organizers of the Google 
Walkout.  
223. Google Walkout for Real Change, “Google Employees and Contractors Participate in ‘Global Walkout 
for Real Change,’” Medium, November 2, 2018, 
https://medium.com/@GoogleWalkout/google-employees-and-contractors-participate-in-global-walk
out-for-real-change-389c65517843. 

61 

 224. Richard Waters, “Google Ends Forced Arbitration for Sexual Harassment Claims,” Financial Times, 
November 8, 2018, https://www.ft.com/content/ce3c11ec-e37e-11e8-a6e5-792428919cee. 
225. Kate Gibson, “Tech Signals End of Forced Arbitration for Sexual Misconduct Claims,” CBS 
MoneyWatch, November 16, 2018, 
https://www.cbsnews.com/news/tech-signals-end-of-forced-arbitration-for-sexual-misconduct-claim
s/. 
226. “AI Now 2017 Report.” 
227. Google Walkout for Real Change, “#GoogleWalkout Update: Collective Action Works, but We Need to 
Keep Working.,” Medium, November 8, 2018, 
https://medium.com/@GoogleWalkout/googlewalkout-update-collective-action-works-but-we-need-t
o-keep-working-b17f673ad513; Noam Scheiber, “Google Workers Reject Silicon Valley Individualism 
in Walkout,” The New York Times, November 7, 2018, 
https://www.nytimes.com/2018/11/06/business/google-employee-walkout-labor.html. 
228. For example, Google’s highly secret Dragonﬂy project to censor search results in China and link 
Chinese residents’ phone numbers to search logs. See: “We Are Google Employees. Google Must 
Drop Dragonﬂy.,” Medium, November 27, 2018, 
https://medium.com/@googlersagainstdragonﬂy/we-are-google-employees-google-must-drop-drago
nﬂy-4c8a30c5e5eb. 
229. Cade Metz, “Tech Giants Are Paying Huge Salaries for Scarce A.I. Talent,” The New York Times, 
October 22, 2017, 
https://www.nytimes.com/2017/10/22/technology/artiﬁcial-intelligence-experts-salaries.html.  
230. “Recommendations” in “AI Now 2016 Report,” (New York: AI Now Institute, 2016), 
https://ainowinstitute.org/AI_Now_2016_Report.pdf. 
231. Crawford and Joler, “Anatomy of an AI System,” https://anatomyof.ai. 
232. See, as just one of many examples: Dastin, “Amazon scraps secret AI recruiting tool that showed bias 
against women,” 
https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-airecruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G. 
233. Lecher, “A Healthcare Algorithm Started Cutting Care, and No One Knew Why.”  
234. Sonia Katyal, “Private Accountability in the Age of the Algorithm,” UCLA Law Review 66 (forthcoming 
2019), https://www.uclalawreview.org/private-accountability-age-algorithm/. 

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