1

The Contestation of Tech Ethics:

A Sociotechnical Approach to Ethics and Technology in Action

Ben Green

bzgreen@umich.edu

Michigan Society of Fellows

Gerald R. Ford School of Public Policy

Abstract

Recent controversies related to topics such as fake news, privacy, and algorithmic bias have 

prompted increased public scrutiny of digital technologies and soul-searching among many of the 

people associated with their development. In response, the tech industry, academia, civil society, 

and governments have rapidly increased their attention to “ethics” in the design and use of digital 

technologies (“tech ethics”). Yet almost as quickly as ethics discourse has proliferated across the 

world of digital technologies, the limitations of these approaches have also become apparent: tech 

ethics is vague and toothless, is subsumed into corporate logics and incentives, and has a myopic 

focus on individual engineers and technology design rather than on the structures and cultures of 

technology production. As a result of these limitations, many have grown skeptical of tech ethics 

and its proponents, charging them with “ethics-washing”: promoting ethics research and discourse 

to defuse criticism and government regulation without committing to ethical behavior. By looking 

at how ethics has been taken up in both science and business in superficial and depoliticizing ways, 

I recast tech ethics as a terrain of contestation where the central fault line is not whether it is 

desirable to be ethical, but what “ethics” entails and who gets to define it. This framing highlights

the significant limits of current approaches to tech ethics and the importance of studying the 

formulation and real-world effects of tech ethics. In order to identify and develop more rigorous 

strategies for reforming digital technologies and the social relations that they mediate, I describe a 

sociotechnical approach to tech ethics, one that reflexively applies many of tech ethics’ own 

lessons regarding digital technologies to tech ethics itself. 

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Table of Contents

1 Introduction: The Crisis of Conscience .................................................................................. 3

2 The Rise of Tech Ethics.......................................................................................................... 6

2.1 Tech Industry .................................................................................................................. 7

2.2 Academia ........................................................................................................................ 7

2.3 Civil Society.................................................................................................................... 8

2.4 Government..................................................................................................................... 9

3 The Limits of Tech Ethics..................................................................................................... 10

3.1 Tech ethics principles are abstract and toothless.......................................................... 10

3.2 Tech ethics is subsumed into corporate logics and incentives...................................... 11

3.3 Tech ethics has a myopic focus on individual engineers and technology design ......... 14

3.4 Tech ethics has become an avenue for ethics-washing................................................. 16

4 The Contestation of Tech Ethics........................................................................................... 18

4.1 Ethics in Science ........................................................................................................... 20

4.2 Corporate Ethics and Co-optation................................................................................. 21

4.3 The Future of Tech Ethics............................................................................................. 23

5 A Sociotechnical Approach to Tech Ethics .......................................................................... 24

5.1 Determinism.................................................................................................................. 25

5.2 Solutionism ................................................................................................................... 25

5.3 Objectivity and Neutrality............................................................................................. 26

5.4 Sociotechnical Systems................................................................................................. 27

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1 Introduction: The Crisis of Conscience

If digital technology production in the beginning of the 2010s was characterized by the brash spirit 

of Facebook’s motto “move fast and break things” and the superficial assurances of Google’s 

motto “don’t be evil,” digital technology toward the end of the decade was characterized by a 

“crisis of conscience” (Marantz, 2019) about these and other technologies’ perils. While many of 

digital technology’s harms were visible early in the decade (not to mention long before), it was not 

until stories of this technology causing harm reached a critical mass that they became salient in the 

public eye. Consider just a few of the cases that have prompted this crisis of conscience within 

tech and the associated “techlash”—the growing animosity of the public toward major technology 

companies—which in 2018 was deemed by both Oxford Dictionaries (Oxford Languages, 2018) 

and the Financial Times (Foroohar, 2018) to be one of the words of the year.

Fake News: Throughout the 2016 U.S. presidential election between Donald Trump and Hillary 

Clinton, social media was plagued with fraudulent stories that went viral: “FBI confirms evidence 

of huge underground Clinton sex network,” “Hillary sold weapons to ISIS,” and more (Emery Jr., 

2016; Ritchie, 2016). Social media platforms—Facebook in particular—came under scrutiny for 

their milquetoast response to fake news, allowing these stories to spread widely without providing 

any indication that they were not true. Given that fake news spreads further, faster, and more 

broadly than true news (Vosoughi, Roy, and Aral, 2018), social media business models of 

prioritizing user engagement appear to be partially responsible for the spread of fake news. In turn, 

numerous commentators—including Hillary Clinton herself—blamed Facebook for Donald 

Trump’s presidential election victory (Blake, 2018; Graham, 2017; Read, 2016; Solon, 2016). 

Later reporting revealed that Facebook’s leadership actively resisted sharing more information 

about Russian efforts to propagate fake news, instead prioritizing the company’s business 

strategies (Perlroth, Frenkel, and Shane, 2018).

Cambridge Analytica: In 2018, The New York Times and The Guardian reported that the voterprofiling firm Cambridge Analytica had harvested information from the Facebook profiles of more 

than 50 million (later revealed to be 87 million) people, without their knowledge or permission, in 

order to target political ads to benefit Donald Trump’s 2016 presidential campaign (Cadwalladr 

and Graham-Harrison, 2018; Lapowsky, 2018; Rosenberg, Confessore, and Cadwalladr, 2018). 

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Cambridge Analytica had acquired this data by exploiting the sieve-like nature of Facebook’s 

privacy policy. These revelations raised new questions about how carefully Facebook protects user 

data and privacy: despite having learned about this data harvesting by 2015, the company did not 

alert users and took only cursory actions to protect the data from further misuse. After the 

Cambridge Analytica story was reported, Congress summoned Mark Zuckerberg to testify about 

Facebook’s practices (Kang et al., 2018) and a concerted effort arose among Facebook users to 

delete their profiles (Hsu, 2018).

Military and ICE Contracts: In 2018, the technology website Gizmodo revealed that Google was 

working with the U.S. Department of Defense (DoD) to develop artificial intelligence software 

that could analyze drone footage (Cameron and Conger, 2018). This effort, known as Project 

Maven, was part of a $7.4 billion investment in AI by the DoD in 2017 (Cameron and Conger, 

2018) and represented an opportunity for Google to gain billions of dollars in future defense 

contracts (Tiku, 2019). The project set off intense debate within Google, as many engineers 

expressed concern about facilitating drone strikes and began organizing to assert that “Google 

should not be in the business of war” (Shane and Wakabayashi, 2018). Project Maven, along with 

similar stories about tech companies partnering with the Trump administration—such as reports 

that Palantir was developing software for Immigration and Customs Enforcement (ICE) to 

facilitate deportations (Woodman, 2017)—prompted new organizing among tech workers and 

computer science students in opposition to tech industry contracts with U.S. defense and 

intelligence agencies, often centered around the slogans #TechWontBuildIt and #NoTechForICE 

(Conger and Metz, 2018; Glaser and Oremus, 2018; Goldberg, 2020; Mijente, 2019).

Algorithmic Bias: In 2016, ProPublica revealed that an algorithm used in criminal courts was 

biased against Black defendants, mislabeling them as future criminals at twice the rates of white 

defendants (Angwin et al., 2016). The dispute about whether the algorithm in question was, in fact, 

biased (Dieterich, Mendoza, and Brennan, 2016; Kleinberg, Mullainathan, and Raghavan, 2016) 

helped build interest in the emerging field of “algorithmic fairness.” Through books such as Cathy 

O’Neil’s Weapons of Math Destruction (O'Neil, 2017), Virginia Eubanks’ Automating Inequality 

(Eubanks, 2018a), and Safiya Noble’s Algorithms of Oppression (Noble, 2018) as well as articles 

demonstrating the biases in algorithms used in contexts ranging from facial recognition 

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(Buolamwini and Gebru, 2018) to hiring (Dastin, 2018), the public began to recognize algorithms 

as both fallible and discriminatory—potentially the source of more harm than good. 

These and other tech-related controversies were a shock to many both inside and outside the world 

of technology, as they arrived in an era of widespread (elite) optimism about the beneficence of 

technology. Yet these controversies also brought public attention to what scholars in fields such 

as STS1, philosophy of science, critical data and algorithm studies, information science, humancomputer interaction, and law have long argued: technology is shaped by social forces, technology 

structures society in often deleterious ways, and technology cannot solve every social problem. 

Broadly speaking, these fields bring a “sociotechnical” approach to studying technologies (digital 

and otherwise), analyzing how technologies shape, are shaped by, and interact with society. The 

sociotechnical frame emphasizes the ways in which social actors and technological artifacts are

intertwined as part of unified—rather than discrete—systems and serve to simultaneously and 

mutually constitute one another (Bijker and Law, 1994; Jasanoff, 2004; Suchman et al., 1999). As 

tech scandals mounted, these fields’ critical and sociotechnical insights, long ignored by most 

technologists and technology journalists, were newly recognized (or in some form recreated).

Many in the tech sector and academia diagnosed these ills as the result of an inattention to ethics: 

a lack of training in ethical reasoning for engineers and a dearth of ethical principles in engineering 

practice, which in turn led to the development of unethical technologies (Fiesler, 2018b; Karoff, 

2019; Marantz, 2019; Raicu, 2017; Zunger, 2018). In response, academics, technologists, 

companies, governments, and more have embraced a broad set of goals often characterized with 

the label “tech ethics”: the introduction of considerations around ethics and social responsibility 

into digital technology education, research, development, use, and governance. In the span of just 

a few years, tech ethics has become a dominant discourse discussed in technology companies, 

academia, civil society organizations, and governments. For those committed to combating an 

array of injustices connected to digital technologies, the rise of tech ethics has produced a range 

of responses: on the one hand, excitement that technologists are increasingly considering their 

social responsibilities and impacts; on the other hand, frustration regarding the limited scope and 

impacts of what tech ethics discourse and practice has thus far entailed. 

1 Referring to the related areas of a) science and technology studies and b) science, technology, and society.

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This essay summarizes these developments and debates in tech ethics. I first describe the primary 

forms of tech ethics and summarize the central critiques made against these efforts, which focus 

on tech ethics’ abstract and toothless nature, corporate logics, narrow and individualistic focus, 

and capacity for strategic use (“ethics-washing”). Against the backdrop of these debates, I then 

turn to describing tech ethics itself as a terrain of contestation, where the central debate is not over 

whether ethics is desirable but over what ethics entails and who obtains the authority to define it. 

These debates suggest the need for a sociotechnical approach to tech ethics that explores the social 

construction and real-world effects of tech ethics. I introduce such an approach through four

frames: determinism, solutionism, objectivity and neutrality, and sociotechnical systems. 

2 The Rise of Tech Ethics 

Although scholars, activists, and others have long considered the ethics and social impacts of 

technology, attention to developing and promoting technology ethics has rapidly grown across the 

tech industry, academia, civil society, and government. Across these sectors, one common 

treatment of tech ethics is through statements of ethical principles. One analysis of 36 prominent 

AI principles documents showed the rapid rise in these statements, from 2 in 2014 to 16 in 2018 

(Fjeld et al., 2020). These documents tend to cover very similar themes, particularly fairness and 

non-discrimination, privacy, accountability, and transparency and explainability (Fjeld et al., 

2020). Many of the documents also reference human rights, with some taking international human 

rights as the framework for analyzing and promoting tech ethics (Fjeld et al., 2020).

The broad and diverse nature of the work connected with tech ethics can make it difficult to define 

where the boundaries of “tech ethics” begin and end. While many people and institutions directly 

embrace the label, others are pursuing related efforts without any direct reference to tech ethics. 

As tech ethics becomes a more widespread frame, however, it is often used as a catchall to refer 

to any effort to study or improve the social impacts of technology. And although sometimes 

directly tied to the philosophical discipline of ethics (i.e., moral philosophy), the “ethics” in tech 

ethics is more frequently tied to applied forms of ethics such as codes of ethics, research ethics, 

and the lived impacts of digital technologies. In order to evaluate the term and the debates that 

surround it, I will largely restrict my discussions of “tech ethics” to those people and organizations 

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that explicitly embrace the label (and its close analogues or derivatives, such as AI ethics and

algorithmic fairness). 

2.1 Tech Industry

The most pervasive treatment of tech ethics within tech companies has come in the form of ethics 

principles and ethics oversight bodies. Companies like Microsoft, Google, and IBM have 

developed and publicly shared AI ethics principles, which include statements such as “AI systems 

should treat all people fairly” and “AI should [...] Be socially beneficial” (IBM, 2018; Microsoft, 

2018; Pichai, 2018). These principles are often supported through dedicated ethics teams and 

advisory boards within companies, with such bodies in place at companies including Microsoft, 

Google, Facebook, Alphabet subsidiary DeepMind, and policing technology company Axon 

(Legassick and Harding, 2017; Nadella, 2018; Novet, 2018; Vincent and Brandom, 2018; Walker, 

2018). Companies such as Google, Accenture, and Clifford Chance have also begun offering tech 

ethics consulting services (Simonite, 2020; Accenture, n.d.; Clifford Chance, n.d.). 

As part of these efforts, the tech industry has formed several coalitions aimed at advancing a 

common dialogue about safe and ethical artificial intelligence. In 2015, Elon Musk and Sam 

Altman (the then-president of the tech incubator Y Combinator) created OpenAI, an independent 

research organization that aims to develop socially beneficial artificial intelligence and mitigate 

the “existential threat” presented by AI, with more than $1 billion in donations from major tech 

executives and companies (Dowd, 2017). A year later, Amazon, Facebook, DeepMind, IBM, and 

Microsoft founded the Partnership on AI (PAI), a nonprofit coalition to shape best practices in AI 

development, advance public understanding of AI, and support socially beneficial applications of 

AI (Finley, 2016; Hern, 2016). 

2.2 Academia

Computer and information science programs at universities have rapidly increased their emphasis 

on ethics training in curricula. While some universities have taught computing ethics courses 

(within both computer science and other fields) for many years (Grosz et al., 2019; Reich et al., 

2020; Shilton et al., 2017), the emphasis on ethics within computing education has increased 

dramatically in recent years (Fiesler, Garrett, and Beard, 2020). When information science 

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professor Casey Fiesler tweeted a link to an editable spreadsheet of tech ethics classes in November 

2017, it quickly grew to a crowdsourced list of more than 200 courses (Fiesler, 2018a). This 

plethora of courses represents a dramatic shift in computer science training and culture, with ethics 

becoming a popular topic of discussion and study after being largely ignored by the mainstream 

of the field just a few years prior.

Research in computer science and related fields has also become increasingly focused on the ethics 

and social impacts of computing. This trend is observable in the recent explosion of conferences 

and workshops related to computing ethics. The ACM Conference on Fairness, Accountability, 

and Transparency (FAccT, formerly FAT*) and the AAAI/ACM Conference on AI, Ethics, and 

Society (AIES) both held their first annual meetings in February 2018 and have since grown 

rapidly. Through 2019, there had been more than 30 workshops related to fairness and ethics at 

major computer science conferences (ACM FAccT Conference, 2020). Many universities have 

supported these curricular and research efforts through the creation of new institutes focused on

the social implications of technology. 2017 alone saw the launch of the AI Now Institute at NYU 

(AI Now Institute, 2017), the Princeton Dialogues on AI and Ethics (Sharlach, 2019), and the 

MIT/Harvard Ethics and Governance of Artificial Intelligence Initiative (MIT Media Lab, 2017). 

More recently formed centers include the MIT College of Computing (MIT News Office, 2018); 

the Stanford Institute for Human-Centered Artificial Intelligence (Adams, 2019); and the 

University of Michigan Center of Ethics, Society, and Computing (Marowski, 2020). 

2.3 Civil Society

Numerous civil society organizations have also coalesced around tech ethics, with strategies that 

include grantmaking and developing principles and toolkits. Organizations such as the MacArthur 

and Ford Foundations have begun exploring and making grants in tech ethics (Robinson and 

Bogen, 2016). For instance, the Omidyar Network, Mozilla Foundation, Schmidt Futures, and 

Craig Newmark Philanthropies partnered on the Responsible Computer Science Challenge, which 

awarded $3.5 million between 2018 and 2020 to support efforts to embed ethics into undergraduate 

computer science education (Mozilla, 2018). Many foundations also contribute to the research, 

conferences, and institutes that have emerged in recent years. 

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Other organizations have been created or have expanded their scope to consider the implications 

and governance of digital technologies. For example, the American Civil Liberties Union (ACLU) 

has begun hiring technologists and is increasingly engaged in debates and legislation related to 

new technology. Organizations such as Data & Society, Upturn, the Center for Humane 

Technology, and Tactical Tech study the social implications of technology and advocate for 

improved technology governance and design practices. 

Many advocates call for engineers to follow an ethical oath modeled after the Hippocratic Oath, 

an ethical oath taken by physicians (Eubanks, 2018a; O'Neil, 2017; Patil, 2018; Simonite, 2018). 

In 2018, for instance, the organization Data for Democracy partnered with Bloomberg and the data 

platform provider BrightHive to develop a code of ethics for data scientists, developing 20 

principles that include “I will respect human dignity” and “It’s my responsibility to increase social 

benefit while minimizing harm” (Data4Democracy, 2018). Former U.S. Chief Data Scientist DJ 

Patil described the event as the “Constitutional Convention” for data science (Eubanks, 2018b). A 

related effort, produced by the Institute for the Future and the Omidyar Network, is the Ethical OS 

Toolkit, a set of prompts and checklists to help technology developers “anticipat[e] the future 

impact of today’s technology” and “not [...] regret the things you will build” (The Institute for the 

Future and Omidyar Network, 2018).

2.4 Government

Many governments have also taken up the mantle of tech ethics, developing commissions and 

principles dedicated to the topic. In the United States, for example, the National Science 

Foundation formed a Council for Big Data, Ethics, and Society (Council for Big Data, 2014), the 

National Science and Technology Council published a report about AI that emphasized ethics 

(National Science and Technology Council, 2018), and the Department of Defense adopted ethical 

principles for AI (U.S. Department of Defense, 2020). Elsewhere, governing bodies in Dubai 

(Smart Dubai, 2018), Europe (European Commission High-Level Expert Group on Artificial 

Intelligence, 2019), Japan (Integrated Innovation Strategy Promotion Council, 2019), and Mexico 

(Martinho-Truswell et al., 2018), as well as international organizations such as the OECD 

(Organisation for Economic Co-operation and Development, 2019) have all put forward 

documents stating principles for ethical AI development. A 2019 analysis of global AI ethics 

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guidelines found 84 such documents (with more than a third from the U.S. and U.K. and none from 

Africa or South America) espousing a common set of principles: transparency, justice and fairness, 

non-maleficence, responsibility, and privacy (Jobin, Ienca, and Vayena, 2019).

3 The Limits of Tech Ethics 

Despite the rapid adoption of “ethics” as an analytic frame for digital technologies, critical analyses 

of these early efforts have indicated that tech ethics suffers from several core limitations. First, the 

actual principles espoused by tech ethics statements are too abstract and lacking in enforcement 

mechanisms to reliably spur ethical behavior in practice. Second, as ethics is incorporated into tech 

companies, ethical ideals are subsumed into corporate logics and incentives. Third, by emphasizing 

the design decisions of individual engineers, tech ethics overlooks the structural forces that shape 

technology’s harmful social impacts. Collectively, these issues suggest that the emphasis on ethics 

represents a strategy of technology companies “ethics-washing” their behavior with a façade of 

ethics while largely continuing with business-as-usual.

3.1 Tech ethics principles are abstract and toothless

Tech ethics codes deal in universal principles (Greene, Hoffmann, and Stark, 2019). In 2016, for 

example, Accenture published a report explicitly outlining “a universal code of data ethics” 

(Accenture, 2016). Professional computing societies also present ethical commitments in a highly 

abstract form, encouraging computing professionals, for instance, “to be ever aware of the social, 

economic, cultural, and political impacts of their actions” and to “contribute to society and human 

well-being” (Stark and Hoffmann, 2019). Ethics codes in computing and information science are 

notably lacking in explicit commitments to normative principles (Stark and Hoffmann, 2019). 

The emphasis on universal principles papers over the fault lines of debate and disagreement that 

spurred the emergence of tech ethics as a widespread discourse in the first place. The recent 

upsurge in tech ethics was prompted by a spate of tech scandals and ensuing critiques of digital 

technologies. Yet the principles that have been developed embody an almost eerie level of 

agreement: two 2019 reports on global AI ethics guidelines similarly emphasized a “global 

convergence” (Jobin, Ienca, and Vayena, 2019) and a “consensus” (Fjeld et al., 2020) in the 

principles espoused. Although these documents tend to reflect a common set of global principles,

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the actual interpretation and implementation of these principles raise substantive conflicts (Jobin, 

Ienca, and Vayena, 2019). The superficial consensus around abstract ideals may thus be hindering 

substantive deliberation regarding whether the chosen values are appropriate, how those values 

should be balanced in different contexts, and what those values actually entail in practice. 

This level of abstraction and consensus is particularly troubling due to a lack of mechanisms to 

enact or enforce the principles embodied in tech ethics principles. When framed at such a high 

level of abstraction, values such as fairness and respect are unable to guide specific actions 

(Mittelstadt, 2019). Nor are these principles binding. The ethics oversight boards in companies 

such as Google and Axon and the ethics principles in companies and governments around the 

world lack the independent authority to veto projects or require certain behaviors (Harwell, 2018; 

Jobin, Ienca, and Vayena, 2019; Knight, 2019). Similarly, professional computing organizations 

such as the IEEE and ACM lack the power to meaningfully sanction individuals who violate their 

codes of ethics (Mittelstadt, 2019). Moreover, unlike fields such as medicine, which has a strong 

and established emphasis on professional ethics, computing lacks a common aim or fiduciary duty 

to unify disparate actors around shared ethical practices (Mittelstadt, 2019). All told, “Principles 

alone cannot guarantee ethical AI” (Mittelstadt, 2019).

3.2 Tech ethics is subsumed into corporate logics and incentives

Digital technology companies have embraced ethics as a matter of corporate concern, stemming 

in large part from the desire to avoid reputational or financial harm. In recent SEC filings, both 

Alphabet and Microsoft noted the potential harms they could face for products that are deemed 

unethical (Simonite, 2019). Corporate tech ethics thus emphasizes the presentation of ethical 

behavior for scrutinizing audiences. An ethnography of ethics work in Silicon Valley found that 

“[p]erforming, or even showing off, the seriousness with which a company takes ethics becomes 

a more important sign of ethical practices than real changes to a product” (Metcalf, Moss, and 

boyd, 2019). For instance, after an effort at Twitter to reduce online harassment stalled, an external 

researcher involved in the effort noted, “The impression I came away with from this experience is 

that [Twitter was] more sensitive to deflecting criticism than in solving the problem of harassment” 

(Seetharaman, 2020).

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Ethics is also framed by companies in terms of its direct alignment with business strategy. A 

software engineer at LinkedIn described algorithmic fairness as being profitable for companies, 

arguing, “If you’re very biased, you might only cater to one population, and eventually that limits 

the growth of your user base, so from a business perspective you actually want to have everyone 

come on board, so it’s actually a good business decision in the long run” (Johnson, 2019). 

Similarly, one of the people behind the Ethical OS toolkit described being motivated to produce 

“a tool that helps you think through [societal] consequences and makes sure what you’re designing 

is good for the world and good for your longer-term bottom line” (Pardes, 2018). 

Finding this alignment between ethics and business is an important task for those charged with 

promoting ethics in tech companies. Recognizing that “[market] success trumps ethics,” 

individuals focused on ethics in Silicon Valley feel pressure to align ethical principles with 

corporate revenue sources (Metcalf, Moss, and boyd, 2019). As one senior researcher in a tech 

company notes, “the [ethics] system that you create has to be something that people feel adds value 

and is not a massive roadblock that adds no value, because if it is a roadblock that has no value, 

people literally won’t do it, because they don’t have to” (Metcalf, Moss, and boyd, 2019). When 

ethical ideals are at odds with a company’s bottom line, they are met with resistance (Marantz, 

2019). 

The emphasis on business strategy creates significant conflicts with ethics in technology 

companies, whose business models often rely on extractive and exploitative practices that were 

partially responsible for the scandals that prompted the “techlash” in the first place. Indeed, 

announcements by Facebook and Twitter that they would invest in privacy, security, and 

combating misinformation and abusive behavior led to rapid declines in the companies’ stock 

values (Neate, 2018; Phillips, 2018). Moreover, even as tech companies espouse a devotion to 

ethics, they continue to develop products and services that raise ethical red flags but promise 

significant profits. For example, even after releasing AI ethics principles that include safety, 

privacy, and inclusiveness (Microsoft, 2018) and committing not to “deploy facial recognition 

technology in scenarios that we believe will put these freedoms at risk” (Smith, 2018), Microsoft 

invested in AnyVision, an Israeli facial recognition company that supports military surveillance of 

Palestinians in the West Bank (Solon, 2019). Microsoft claimed that AnyVision complied with its 

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ethics principles (even while former AnyVision employees admitted that the company did not meet 

Microsoft’s ethical standards), suggesting either that these principles are so flexible as to allow 

such applications or that they are followed only when doing so is convenient.

These examples indicate that tech ethics is being subsumed into existing tech company logics and 

business practices rather than meaningfully challenging or changing those logics and practices

(even if some individuals within companies do want to create meaningful change). This absorption 

allows companies to take up the mantle of caring about ethics without making substantive changes 

to their processes or business strategies. The goal in companies is to find practices “which the 

organization is not yet doing but is capable of doing” (Metcalf, Moss, and boyd, 2019), indicating 

an effort to find relatively costless reforms that provide the veneer of ethical behavior. Ethics vision 

statements “co-opt the language of some critics,” taking critiques grounded in a devotion to equity 

and social justice and turning them into ethical discussions that are akin to “conventional business 

ethics” (Greene, Hoffmann, and Stark, 2019). As they integrate these principles into new practices 

and products, tech companies “are learning to speak and perform ethics rather than make the 

structural changes necessary to achieve the social values underpinning the ethical fault lines that 

exist” (Metcalf, Moss, and boyd, 2019).

These limits to corporate tech ethics are exemplified by Google’s firing of Timnit Gebru (and later 

Meg Mitchell) following the company’s concerns about a paper examining the limitations and 

harms of large language models, which are central to Google’s business (Hao, 2020). Despite 

Gebru’s and Mitchell’s supposed charge as co-leads of Google’s ethical AI team, Google objected 

to a paper they had written with several internal and external colleagues about ethical concerns 

related to these AI models, suggesting that the authors were insufficiently attentive to recent 

technical advances that mitigate these concerns (Hao, 2020). Soon after, journalists revealed that 

Google had expanded its review of papers that discuss “sensitive topics,” telling researchers, for 

instance, to “take great care to strike a positive tone” regarding Google’s technologies and products 

(Dave and Dastin, 2020). Thus, even as Google publicly advertised its care for the ethics of its 

technologies, internally the company was acting strongly to curtail criticisms that it deemed 

threatening to its core business interests. 

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3.3 Tech ethics has a myopic focus on individual engineers and technology design 

Tech ethics typically emphasizes the roles and responsibilities of engineers, paying relatively little 

attention to the broader environments in which these individuals work. Although professional 

codes in computing and related fields state general commitments to the public, profession, and 

one’s employer, “the morality of a profession’s or an employer’s motives are not scrutinized” 

(Stark and Hoffmann, 2019). Similarly, ethics within computer science curricula tends to focus on 

ethical decision making for individual engineers (Silbey, 2018). 

From this individualistic frame comes an emphasis on appealing to the good intentions of 

engineers, with the assumption that better design practices and procedures will lead to better 

technology. Ethics becomes a matter of individual engineers and managers “doing the right thing” 

“for the right reasons” (Metcalf, Moss, and boyd, 2019). Efforts to provide ethical guidance for 

tech CEOs rest on a similar logic: “if a handful of people have this much power—if they can, 

simply by making more ethical decisions, cause billions of users to be less addicted and isolated 

and confused and miserable—then, isnʼt that worth a shot?” (Marantz, 2019). The broader public 

beyond technical experts is not seen as having a role in defining ethical concerns or shaping the 

responses to these concerns (Greene, Hoffmann, and Stark, 2019).

Tech ethics therefore centers debates about how to build better technology rather than whether or 

in what form to build technology (let alone who gets to make such decisions). Underlying ethics 

work across academia, civil society, and tech companies is an assumption that artificial intelligence 

and machine learning are “inevitable,” such that “‘better building’ is the only ethical path forward” 

(Greene, Hoffmann, and Stark, 2019). In turn, tech ethics diagnoses the harmful social 

consequences of technology as treatable through technical and procedural solutions (Metcalf, 

Moss, and boyd, 2019). Ethics teams within tech companies have developed and shared numerous 

ethics and fairness toolkits, including Datasheets at Microsoft (Gebru et al., 2018), Model Cards 

at Google (Mitchell et al., 2019), AI Fairness 360 at IBM (Varshney, 2018), the Fairness Tool at 

Accenture (Peters, 2018), and Fairness Flow at Facebook (Gershgorn, 2018). 

Although efforts such as these stand to remedy certain harms that result from digital technology, 

approaches to tech ethics that focus on the design decisions of engineers omit much of what 

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scholarship in STS, law, and other fields has diagnosed regarding the source of social harms 

connected to digital technologies. For example, scholars and journalists have articulated the social 

harms associated with business models that rely on collecting massive amounts of data about the 

public (Schneier, 2016; Zuboff, 2018), companies that wield monopolistic power (Khan, 2017; 

Wu, 2018), technologies that are built through the extraction of natural resources and the abuse of 

workers (Crawford and Joler, 2018; Dobbe and Whittaker, 2019; Evans, 2019; Gray and Suri, 

2019), and the exclusion of women, minorities, and non-technical experts from decisions with 

significant social impacts (Jasanoff, 2006; West, Whittaker, and Crawford, 2019). To the extent 

that efforts based in individual design decisions are taken as the heart of what it means to “do” 

ethics, they bear the “risk of a premature foreclosure of the fundamentally open-ended and 

irresolvable questions that underlie human value commitments” into a set of procedures that must 

be followed (Metcalf, Moss, and boyd, 2019).

The focus on improving design also relies on a narrow theory of change for how to reform 

technology. Regardless of their intentions and the design frameworks at their disposal, individual 

engineers typically have little power to shift corporate strategy. Executives can limit which teams 

know about a project and what they know about it, enforcing strict secrecy and segmenting project 

teams to prevent knowledge and internal dissent about controversial projects (Conger and Metz, 

2018; Gallagher, 2018). Even when engineers do know about and protest projects, the result is 

often them resigning or being replaced rather than the company changing course (Conger and 

Metz, 2018; Simonite, 2018). Instead of efforts focused explicitly on design and ethics, many 

improvements in tech systems and new regulations have been the result of collective action among 

tech workers as well as external pressure and organizing from activists, journalists, workers, and 

scholars (Crawford et al., 2019; Haskins, 2020). 

These structural conditions place significant barriers on the benefits that design-oriented tech 

ethics will be able to achieve. As MIT anthropologist Susan Silbey notes in regard to teaching 

engineering ethics, “while we might want to acknowledge human agency and decision-making at 

the heart of ethical action, [...] we blind ourselves to the structure of those choices—incentives, 

content, and pattern—if we focus too closely on the individual and ignore the larger pattern of 

opportunities and motives that channel the actions we call ethics” (Silbey, 2018). By ignoring the 

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contexts of technology production and how technology interacts with society, we risk tinkering on 

the margins of technology, developing a lingo and practices that come to define ethical behavior 

while leaving in place the structures that generated the controversies spurring the rise of tech ethics. 

3.4 Tech ethics has become an avenue for ethics-washing

As evidence of tech ethics’ limitations has grown, many have critiqued tech ethics as a strategic 

effort among technology companies to quell public scrutiny rather than as a noble effort to take 

responsibility for technology’s social impacts. This strategy has been labeled “ethics-washing” 

(i.e., “ethical white-washing”): using the language of ethics to paint a superficial portrait of ethical 

behavior in order to avoid heightened public backlash and the introduction of regulations that 

would require substantive concessions (Metzinger, 2019; Nemitz, 2018; Wagner, 2018). 

In other words, ethics discourse has become a convenient way for tech companies to defuse 

criticism and regulation by creating structures for self-governance without any commitment to 

meaningfully altering their behavior. As an ethnography of ethics in Silicon Valley found, “It is a 

routine experience at ‘ethics’ events and workshops in Silicon Valley to hear ethics framed as a 

form of self-regulation necessary to stave off increased governmental regulation” (Metcalf, Moss, 

and boyd, 2019). Recognizing this strategy casts important “flaws” of tech ethics as features rather 

than bugs: by focusing public attention on the actions of individual engineers and particular 

technical limitations (such as algorithmic bias), companies perform a sleight-of-hand that shifts 

structural questions about power and profit out of view. 

Thomas Metzinger, a philosopher who served on the European Commission’s High-Level Expert 

Group (HLEG) on Artificial Intelligence to develop AI ethics guidelines (European Commission 

High-Level Expert Group on Artificial Intelligence, 2019), provides a particularly striking account 

of ethics-washing in action (Metzinger, 2019). The HLEG on AI contained only four ethicists out 

of 52 total people and was dominated by representatives from industry. Metzinger’s own work to 

develop “Red Lines” that AI applications should not cross was “watered down” by industry

representatives eager for a “positive vision” for AI. All told, Metzinger describes the HLEG’s 

guidelines as “lukewarm, short-sighted and deliberately vague” and concludes that the tech 

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industry is “using ethics debates as elegant public decorations for a large-scale investment 

strategy” (Metzinger, 2019).

Tech companies have further advanced this “ethics-washing” agenda through funding academic 

research and events. Many of the scholars writing about tech policy and ethics are funded by 

Google, Microsoft, and others, yet often do not disclose this funding (Google Transparency 

Project, 2017; Williams, 2019). Tech companies also provide funding for prominent academic 

conferences, including the ACM Conference on Fairness, Accountability, and Transparency

(FAccT); and the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES); 

and the Privacy Law Scholars Conference (PLSC). Even if these funding practices do not directly 

influence the research output of individual scholars, they allow tech companies to shape the 

broader academic and public discourse regarding tech ethics, raising up certain voices and 

conversations at the expense of others.2

Further debate about the value and impacts of tech ethics erupted in December 2019, spurred by 

an article written by MIT graduate student Rodrigo Ochigame (Ochigame, 2019) in the wake of 

the revelations that the MIT Media Lab—a center that conducts research and provides grants 

related to tech ethics—had received secret funding from Jeffrey Epstein, the financier charged with 

sex trafficking of minors (Farrow, 2019; Tracy and Hsu, 2019). Describing his experiences 

working in Joi Ito’s AI ethics group at the Media Lab and collaborating with the Partnership on 

AI, Ochigame articulated how “the discourse of ‘ethical AI’ [...] was aligned strategically with a 

Silicon Valley effort seeking to avoid legally enforceable restrictions of controversial 

technologies” (Ochigame, 2019). Ochigame described witnessing firsthand how the Partnership 

on AI made recommendations that “aligned consistently with the corporate agenda” by reducing 

political questions about the criminal justice system to matters of technical consideration. A central 

part of this effort was tech companies strategically funding researchers and conferences in order 

to generate a widespread discourse about “ethical” technology. Finding that “the corporate lobbyʼs 

effort to shape academic research was extremely successful,” Ochigame concluded that “[b]ig tech 

money and direction proved incompatible with an honest exploration of ethics.”

2 The integrity of academic tech ethics has been further called into question due to funding from other sources beyond 

tech companies (Domínguez et al., 2019; Farrow, 2019; Mboya, 2019).

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Some believed that Ochigame oversimplified the story, failing to fully credit the many people 

behind tech ethics and the reforms that this movement have prompted (Darling, 2019; Epstein, 

2019; Sinders, 2019). On this view, all of the work by scholars and activists increasing attention 

to technological harms and pushing for legislation was worthy of praise as more than just corporate 

capture. Yet many of the people (often activists and scholars of color) centrally involved in efforts 

to expose and resist the harms of digital technology see their work as distinct from tech ethics, 

which represents the narrow domain of efforts typically promulgated by tech companies. Safiya 

Noble described Ochigame’s article as “All the way correct and worth the time to read” (Noble, 

2019). Lilly Irani and Ruha Benjamin expressed similar sentiments, noting that “AI ethics is not a 

m[o]v[e]m[e]nt” (Irani, 2019) and that “many of us don’t frame our work as ‘ethical AI’” 

(Benjamin, 2019).

This debate in response to Ochigame’s article exposed the fault lines at the heart of tech ethics. 

While tech ethics is often framed as encompassing the broad societal debates about the social 

impacts of technology, many of the people at the forefront of those efforts see tech ethics as the 

narrower (typically industry-led) efforts to promote “ethics” in technology. Where, then, do the 

bounds of tech ethics lie? Who is behind tech ethics: tech companies, or the activists and scholars 

who are highlighting harmful technologies and advocating for reforms? What are the appropriate 

strategies for pursuing ethical technology: through reforming design practices, or through 

reforming the political and economic forces that generate and govern technology? And, perhaps 

most fundamentally, what is the value of tech ethics: does it provide valuable reforms (even if 

incremental ones), or is it an active hindrance to achieving more just technology? The answers to 

these contested questions will shape the future of efforts to pursue more just digital infrastructures 

and technologies. 

4 The Contestation of Tech Ethics 

The debates described in the previous section reveal that the central question regarding tech ethics 

is not over whether it is desirable to be ethical, but over what “ethics” entails and who gets to 

define it. In this sense, ethics is an “essentially contested concept” (Gallie, 1955), where the central

debates regard the meaning—rather than desirability—of ethics. Akin to the debates described 

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above, the “ethics” in tech ethics tends to take on four overlapping yet often conflicting meanings: 

moral justice, corporate values, legal risk, and compliance (Moss and Metcalf, 2020). 

Such definitional debates have significant stakes. In the context of antidiscrimination law, 

Kimberlé Crenshaw describes the “definitional tension” between an “expansive view” of 

antidiscrimination that strives to eradicate the oppression of Blacks and a “restrictive view” of 

antidiscrimination that aims only to prevent discrimination based explicitly on race (Crenshaw, 

1988). In any given instance, “specific interpretations proceed largely from the world view of the 

interpreter” (Crenshaw, 1988). Similarly, different interpretations of “tech ethics” suggest 

drastically different paths forward: tech ethics embodies a conflict between an expansive view that 

aims to remedy a broad range of injustices associated with digital technologies and a restrictive 

view that aims to limit a specific set of tangible harms caused by these technologies. Because of 

these conflicting accounts co-existing within the same term, to call for tech ethics is, in Crenshaw’s 

terms, “to demand nothing specific” (Crenshaw, 1988). 

In this sense, tech ethics is itself a terrain of contestation whose impacts hinge on who is able to 

demarcate the bounds of legitimate authority regarding what ethics entails in relation to technology 

and who is responsible for technologies’ social impacts. Whether it be technology companies 

projecting procedural toolkits as solutions to ethical dilemmas, computer scientists reducing 

normative questions into mathematical metrics, academic tech ethics institutes being funded by 

billionaires and led primarily by white men (Gershgorn, 2019), or tech ethics principles being 

disseminated predominantly by the U.S. and Western Europe, the contestation of tech ethics 

centers on certain actors attempting to acquire or maintain authority over what it means for 

technology to be “ethical,” at the expense of others. These efforts embody practices of “boundarywork,” which involves drawing boundaries between legitimate and illegitimate sources of 

authority (Gieryn, 1983). Drawing these boundaries can help scientists acquire intellectual 

authority and maintain professional autonomy, often in ways that exclude women, minorities, the 

Global South, and other publics (Collins, 2000; Haraway, 1988; Visvanathan, 2005).

Examples of how ethics has been implemented in two other domains—science and business—shed 

light on the nature and stakes of present debates about tech ethics. 

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4.1 Ethics in Science

Many areas of science have embraced ethics in recent decades in response to debates about the 

social implications of emerging research and applications. Despite the seeming promise of ethics 

in science, however, existing approaches to promoting ethics in science are limited in their ability 

both to raise debates about the structure and values of science and to promote democratic 

governance of science. Science ethics bodies suffer from limited “ethical imaginations” and are 

often primarily concerned with “keep[ing] the wheels of research turning while satisfying publics 

that ethical standards are being met” (Jasanoff, 2016). “Ethical analysis that does not advance such 

instrumental purposes tends to be downgraded as not worthy of public support” (Jasanoff, 2016).

Rather than interrogating fundamental questions about the purposes of research or who gets to 

shape that research, ethics has become increasingly institutionalized, instrumentalized, and 

professionalized, with an emphasis on filling out forms and checking off boxes (Jasanoff, 2016; 

Reardon, 2013). In turn, “systems of ethics [...] play key roles in eliding fundamental social and 

political issues” that inhere in the conception and development of research (Reardon, 2011). For 

instance, efforts to introduce ethics into genetic research throughout the 1990s and 2000s treated 

ethics “as something that could be added onto science—and not something that was unavoidably 

implicit in it,” thus obscuring the “bioconstitutional” questions about racial groups at the heart of 

genetic testing projects (Reardon, 2011). Because “ethical choices inhered in efforts to study 

human genetic variation, regardless of any explicit effort to practice ethics,” these research 

projects “bypass[ed] responsibility for their roles in co-constituting natural and moral orderings of 

human difference, despite efforts to address ethics at the earliest stages of research design” 

(Reardon, 2011). 

The turn to ethics can also entail an explicit effort among scientists to defuse external scrutiny and 

to develop a regime of self-governance. For example, biologists in the 1970s, frightened by calls 

for greater public participation in genetic engineering, organized a conference at the Asilomar 

Conference Center in California. The scientific community at Asilomar pursued two, intertwined 

goals. In order to present a unified and responsible public image, the Asilomar organizers restricted 

the agenda to eschew discussions of the most controversial applications of genetic engineering 

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(biological warfare and human genetic engineering). And in order to convince the American public 

and politicians to allow biologists to self-govern their pursuit of genetic engineering, the Asilomar 

attendees “redefined the genetic engineering problem as a technical one” that only biologists could 

credibly discuss (Wright, 2001). Although often hailed as a remarkable occasion of scientific selfsacrifice for the greater good, accounts from the conference itself demonstrate that “[s]elf-interest, 

not altruism, was most evident at Asilomar,” as not making any sacrifices and appearing selfserving would have only invited stringent, external regulation (Wright, 2001).

Tech ethics mirrors many of these attributes in scientific ethics. As with ethics in other fields of 

science, tech ethics involves a significant emphasis on institutionalized design practices, often 

entailing checklists and worksheets. Mirroring ethics in genetic research, the emphasis on ethical 

design in computer science departments and tech companies treats ethics as something that can be 

added on to digital technologies by individual engineers, overlooking the epistemologies and 

economic structures that underlie these technologies and their harms. Furthermore, tech companies 

and computer scientists focusing on technical challenges such as algorithmic fairness mirror the 

strategic efforts of molecular biologists at Asilomar to reframe moral questions as technical 

questions in order to retain self-regulation.3 The removal of red lines in the European 

Commission’s High-Level Expert Group on AI bears a striking resemblance to the exclusion of 

controversial topics from the agenda at Asilomar. 

4.2 Corporate Ethics and Co-optation

Codes of ethics have long been employed by groups of experts (e.g., doctors and lawyers) to codify 

a profession’s expectations regarding culture and behavior and to shore up the profession’s public 

reputation (Abbott, 1983; Metcalf, 2014). Similarly, companies across a wide range of sectors have 

embraced ethics codes, particularly over the past half century and typically in response to public 

perceptions of unethical behavior (Wood and Rimmer, 2003). 

Yet it has long been clear that the public benefits of corporate ethics codes are minimal. While 

ethics codes can help make a group appear ethical, on their own they do little to promote a culture 

3

In an ironic parallel, the Future of Life Institute organized an Asilomar Conference on Beneficial AI in 2017, leading 

to the development of 23 “Asilomar AI Principles” (Future of Life Institute, 2017).

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of ethical behavior (Wood and Rimmer, 2003). The primary goal of business ethics has instead 

been the “inherently unethical” motivation of corporate self-preservation: to reduce public and 

regulatory scrutiny by promoting a visible appearance of ethical behavior (Cressey and Moore, 

1983; Wood and Rimmer, 2003). This emphasis on corporate reputation and profit is facilitated by 

ethics codes making universal moral claims that “are extremely important as claims but extremely 

vague as rules” and emphasizing individual actors and behaviors, leading to a narrow, “one-caseat-a-time approach to control and discipline” (Abbott, 1983). Ethics codes in the field of 

information systems have long exhibited a notable lack of explicit moral obligations for computing 

professionals (Oz, 1992; Stark and Hoffmann, 2019). 

Business ethics is indicative of the broader phenomenon of co-optation: an institution 

incorporating elements of external critiques from groups such as social movements—often gaining 

the group’s support and improving the institution’s image—without meaningfully acting on that 

group’s demands or providing that group with decision-making authority (Gamson, 1975; 

Selznick, 1948; Trumpy, 2014). The increasing centrality of companies as the target of social 

movements has led to a particular form of co-optation called “corporatization,” in which 

“corporate interests come to engage with ideas and practices initiated by a social movement and, 

ultimately, to significantly shape discourses and practices initiated by the movement” (King and 

Busa, 2017). Through this process, large corporate actors in the United States have embraced 

“diluted and deradicalized” elements of social movements “that could be scaled up and adapted 

for mass markets” (King and Busa, 2017). Two factors make movements particularly susceptible 

to corporatization: heterogeneity—movement factions that are willing to work with companies 

gain influence through access to funding—and materiality—structural changes get overlooked in 

favor of easily commodifiable technological “fixes.” By participating in movement-initiated 

discourses, companies are able to present themselves as part of the solution rather than part of the 

problem, and in doing so can avoid more restrictive government regulations.

Tech ethics embodies many of the attributes of corporate ethics, particularly the significant efforts 

to shore up legitimacy and avoid external regulation. Abstract and individualized tech ethics codes 

reproduce the virtue signaling and self-preservation behind traditional business ethics. And in a 

notable example of co-optation and corporatization, technology companies have promoted tech 

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ethics as a diluted and commoditized version of tech-critical discourses that originated largely 

from outside of technology circles. Because societal efforts to improve technology are often aimed 

at companies and include both heterogeneity and materiality, it is particularly vulnerable to 

corporatization. Through this process of corporatization, tech companies are using tech ethics to 

present themselves as part of the solution rather than part of the problem and are using funding to 

empower the voices of certain scholars and academic communities. The power of this influence 

can be seen in the expanding scope of work that is published and discussed at conferences, 

workshops, and other events under the banner of “tech ethics.” Even scholars who do not embrace 

the tech ethics label are increasingly subsumed into this category, either lumped into it by others 

or compelled into it as opportunities to publish research, impact policymakers, and receive grants 

are increasingly shifting to the terrain of “ethics.”

4.3 The Future of Tech Ethics

Drawing on these two examples of ethics in action leads to two conclusions about tech ethics. 

First, the parallels to ethics in science and business indicate that current tech ethics discourse is 

likely to enable technologists and technology companies to label themselves as “ethical” without 

substantively altering their practices. Although there are multiple notions of tech ethics currently 

coexisting—many individuals and groups, including some within tech companies, are pursuing 

expansive forms of tech ethics—the influence of tech companies makes it likely that their narrow

vision of “tech ethics” will subsume any others. Furthermore, many of the most prominent voices 

regarding tech ethics are white men who want to claim expertise and thought leadership while 

ignoring the work of established fields and scholars, many of whom are women and people of 

color (Irani and Chowdhury, 2019; Mozilla, 2020). To the extent that tech ethics continues to 

follow the path of science ethics, business ethics, and corporatization, tech companies and others 

will be able to define ethics in such a way that technologies and technology companies are deemed 

“ethical” even while continuing to produce significant social harm. For the tech companies that 

dominate tech ethics discourse, such narrowing is precisely the point. 

Second, rather than treating “ethics” as well-defined and inherently desirable, those striving for 

substantive and structural improvements in digital technologies must look to the formulation and 

real-world effects of “tech ethics.” The examples of science and business ethics indicate that ethics 

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in practical contexts can be quite distinct from the normative demands and moral inquiry that gave 

rise to the embrace of ethics in the first place. Just as digital technology is often applied as a 

solution to social problems, tech ethics is today being applied as a solution to sociotechnical 

problems. As with technology itself, tech ethics is a tool with particular affordances that is being 

developed to serve social purposes. If “technologies can be assessed only in their relations to the 

sites of their production and use” (Suchman et al., 1999), then so too, we might say, tech ethics 

can be assessed only with regard to how it is conceived and how it affects the world in practice.

Thus, rather than presenting a unifying and beneficent set of principles and practices, tech ethics 

has emerged as a central site of struggle regarding the future of digital architectures, governance,

and economies. As was hinted at in the varied responses to Ochigame’s critique of ethics-washing, 

many of the more radical critics of technology and inequality see themselves as outside of (if not 

in opposition to) the dominant strains of tech ethics. Discourses and practices of resistance (AntiEviction Mapping Project, 2020), defense (Lewis et al., 2018), abolition (Hamid, 2020; Stop 

LAPD Spying Coalition and Free Radicals, 2020), and decentering technology (Gangadharan and 

Niklas, 2019) have also emerged (often from activists and communities rather than academics) in 

response to the social injustices meted out through digital technologies. Although some may see 

all of these efforts as falling under the broad umbrella of an expansive tech ethics, these nascent 

movements embody distinct aspirations from the narrow mainstream of tech ethics. Conflating 

them under a common label risks giving tech ethics the imprimatur of radical, justice-oriented 

work even as its core tenets and practices eschew such commitments. Efforts to resist oppressive 

technological architectures and to study or support such efforts must be attentive to these porous 

and slippery boundaries.

5 A Sociotechnical Approach to Tech Ethics

Given these dynamics of contestation surrounding tech ethics, ethics will not, on its own, provide 

a salve for technology’s social harms. Nonetheless, taking a reflexive (Bloor, 1991) approach that 

applies core elements of tech ethics’ analyses of digital technology to tech ethics itself contains 

the seeds of a more robust approach to pursuing a more just society, digital and otherwise. One 

dimension of tech ethics (and critiques of tech ethics) is a focus on technology’s social impacts 

and on how technology interacts with society—in other words, approaching digital technologies 

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through a sociotechnical analysis. This sociotechnical approach, drawing on STS, has much to 

offer engineering ethics, shedding light on the responsibilities of engineers and how artifacts shape 

and are shaped by the social world (Johnson and Wetmore, 2007). This suggests the value of an 

approach to tech ethics that mirrors the sociotechnical approach to technology—one that can 

inform our understanding of the responsibilities of those behind tech ethics and how tech ethics 

shapes and is shaped by the social world. With this aim in mind, it is fruitful to consider tech ethics 

through the lens of four sociotechnical frames: determinism, solutionism, objectivity and 

neutrality, and sociotechnical systems.

5.1 Determinism

A central component of a sociotechnical approach to technology is rejecting technological 

determinism: the belief that technology evolves autonomously and determines social outcomes 

(Dafoe, 2015; Marx and Smith, 1994). Such an approach has been rejected through scholarship 

demonstrating that even as technology plays some role in shaping society, so too, simultaneously, 

does society shape technology (Bijker and Law, 1994; Jasanoff, 2004; Pinch and Bijker, 1987; 

Winner, 1986). Ethics in digital technology is today being treated through a similar sort of “ethical 

determinism,” with an underlying assumption that adopting “ethics” will lead to ethical 

technologies. Yet in both science and business there is a long history demonstrating that such an 

embrace of ethics is not sufficient to prompt substantive changes in behavior, and there is emerging 

evidence of the same limitation with regard to tech ethics. As with technology, ethics does not 

emerge in a vacuum and does not on its own determine sociotechnical outcomes. What is needed, 

then, is a new approach that looks to the sociotechnical complexities of tech ethics: how it shapes

the development and governance of technologies (and collective understandings of technologies) 

as well as how ethical discourses and practices are themselves shaped by a variety of social forces. 

5.2 Solutionism

Closely intertwined with a belief in technological determinism is the practice of technological 

solutionism: the expectation that technology can and will solve all social problems (Morozov, 

2014). A great deal of scholarship has demonstrated how technological solutions not only typically 

fail to provide the intended solutions, but also can exacerbate the problems they are intended to 

solve (Ames, 2019; Green, 2019; Morozov, 2014; Toyama, 2015). Yet even as tech ethics debates

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have highlighted how technology is not always the answer to social problems, a common response

has been to embrace an “ethical solutionism”: promoting ethics principles and practices as the 

solution to these sociotechnical problems. A notable example (at the heart of many tech ethics 

agendas) is the response to algorithmic discrimination through algorithmic fairness, which often 

centers narrow mathematical definitions of fairness but leaves in place the structural and systemic 

conditions that generate a great deal of algorithmic harms (Green, 2020; Hoffmann, 2019). Efforts 

to introduce ethics across science, business, and technology function similarly, providing an 

addendum of ethical language and practices on top of existing structures and epistemologies which 

themselves go largely uninterrogated. Thus, just as technical specifications of algorithmic fairness 

are insufficient to guarantee fair algorithms, tech ethics principles are insufficient to guarantee 

ethical technologies. Ethics principles, toolkits, and other mechanisms are just one component of 

what must be a broad array of approaches to improve technology. 

5.3 Objectivity and Neutrality

Debates about biased and harmful technologies have led to an increasingly widespread recognition 

in computer science and the tech industry of notable insights from STS: engineers are not objective 

and technology is not neutral. It is clear that improving digital technologies requires grappling with 

the normative commitments of engineers and incorporating more voices into the design of 

technology (Costanza-Chock, 2020; Green and Viljoen, 2020). Yet even as tech ethics emphasizes 

principles such as fairness and inclusiveness, the range of perspectives remains quite narrow and 

ethics is treated as an objective, universal body of principles (Fjeld et al., 2020; Greene, Hoffmann, 

and Stark, 2019; Jobin, Ienca, and Vayena, 2019). The consensus around particular ethical 

principles may therefore say less about the objective universality of these ideals than about the 

narrow range of perspectives that have been given voice regarding tech ethics. In many cases, 

white and male former technology company employees are cast to the front lines of public 

influence regarding tech ethics (Irani and Chowdhury, 2019; Mozilla, 2020). Rather than treating 

tech ethics as the search for objective and universal moral principles, it is necessary to grapple 

with the standpoints and power of different actors, the normative principles embodied in different 

ethical frameworks, and potential mechanisms for adjudicating between conflicting ethical 

commitments.

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5.4 Sociotechnical Systems

A key result of treating technologies as embedded within sociotechnical systems is expanding the 

frame of analysis beyond the technical artifact itself: rather than technical features fully 

determining an artifact’s social impacts, the artifact and the social world “co-produce” social 

outcomes (Jasanoff, 2004). Technologies are not discrete objects that can be properly evaluated in 

the abstract from the context of their use (Suchman et al., 1999). Indeed, many of the animating 

concerns and critiques of tech ethics connect to harms that arise when digital technologies are 

integrated into society without proper attention to the social context and human interactions that 

will shape its impacts (Green and Chen, 2019; Green and Viljoen, 2020; Rose, 2019; Vincent, 

2016; Vosoughi, Roy, and Aral, 2018). Nonetheless, efforts to promote ethical technology 

typically focus on the internal characteristics of tech ethics—which principles to promote, for 

instance—with little attention to the impacts that these efforts will or will not have when integrated 

into larger settings such as a tech company or computer science curriculum. In turn, tech ethics 

has had limited effects on technology production and has played a sometimes-legitimizing role for 

technology companies. But just as the “unintended consequences” of technology often represents 

a failure to consider how artifacts might be used or abused in practice (Jasanoff, 2016), these 

limited impacts of tech ethics must not be seen as having been impossible to predict, particularly 

given the precedents of science ethics, business ethics, and corporate co-optation. Rather than 

treating tech ethics as being defined by its formal characteristics such as the principles espoused, 

any attempts to promote more ethical technology must operate with an eye toward the many factors 

that will shape the real-world impacts of tech ethics efforts. This includes considering how central 

framings (such as ethics) could be redefined and wielded by different actors as well as how to 

robustly embody moral principles in the procedures involved in developing, evaluating, and 

deploying technology. 

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Acknowledgments

I thank Elettra Bietti, Anna Lauren Hoffmann, Jenny Korn, Kathy Pham, and Luke Stark for their 

comments on this essay. I also thank the Harvard STS community, particularly Sam Weiss Evans, 

for feedback and direction on an earlier iteration of this chapter.

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