• Skip to main content
  • Skip to primary sidebar
  • Skip to footer
  • Core Principles of Responsible AI
    • Accountability
    • Fairness
    • Privacy
    • Safety and Security
    • Sustainability
    • Transparency
  • Special Topics
    • AI in Industry
    • Ethical Implications
    • Human-Centered Design
    • Regulatory Landscape
    • Technical Methods
  • Living Dictionary
  • State of AI Ethics
  • AI Ethics Brief
  • 🇫🇷
Montreal AI Ethics Institute

Montreal AI Ethics Institute

Democratizing AI ethics literacy

Owning Ethics: Corporate Logics, Silicon Valley, and the Institutionalization of Ethics (Research Summary)

December 19, 2020

Summary contributed by Nga Than (@NgaThanNYC), a doctoral candidate in the Sociology program at City University of New York – The Graduate Center.

[Link to original paper + authors at the bottom]


Overview: This paper outlines the role of “ethics owners,” a new occupational group in the tech industry, whose jobs are to examine ethical consequences of technological innovations. The authors highlight competing logics that they have to navigate, and two ethical pitfalls that might result from those different imperatives.


Google’s recent firing of Timnit Gebru, a prominent AI ethics researcher, has shaken the AI ethics community. The event has called into question two main issues in the tech industry: its lack of diversity, and its faulty relationship with ethical considerations of technological development. Dr. Gebru represents the growing “ethics owners class of tech workers” who champion ethical causes, ethical designs, development, and deployment of technology from within the tech industry. This research article provides a conceptual framework to understand this emerging occupation and the various day-to-day struggles that these ethics professionals like Dr. Gebru are facing within the industry. 

The authors employed a mixed qualitative methods approach by gathering ethnographic, textual and interviewing data. They interviewed 17 ethics professionals from different well-known companies whose formal roles have been to address ethics within their companies or within the industry. This particular role has become institutionalized after a series of public scandals such as Cambridge Analytica in the 2016 US presidential election, racial biases in facial recognition technology broke out. 

Ethics owners’ daily activities are to examine the social consequences of technology products. Their jobs are similar, but not the same as the business ethics, legal ethics, and sometimes PR teams. They respond to external pressures to tech companies within corporate boundaries. Sometimes they are seasoned engineers, MBA holders, trained in social sciences, or humanities. This class of tech workers are trying to change the tech industry from within by “fulfilling fundamental ethics commitments.” The article defines the process of institutionalization of ethics “as a set of roles, and responsibilities,” and “operationalized as a set of practices and procedures.” They argue that ethics owners “operate inside a fraught dynamic.” While “attempting to resolve critical external normative claims about the core logics of the tech industry,” they have to do so within the corporate structure, and being embedded within corporate logics. This might lead to structural, cultural and social pitfalls. 

The authors use the ethnographic approach to ethics or the “ordinary ethics approach.” Instead of thinking about ethics as a set of abstract concepts, and principles, they examine “how ethics and morality structure social life,” and “how everyday practices reveal the moral commitments embedded in actions.” They found that “ethics as everyday practice” meets with challenges because tech workers, managers, and other stakeholders are not necessarily aware of ethics. Inside these companies, ethics owners are in charge of developing “strategies to align everyday practices with corporate logics” while navigating the everydayness of corporate life. They actively define, and help their companies to locate where ethics responsibility lies within the organizational hierarchy. 

The role of ethics owners within the tech industry is ambivalent. Ethics owners operating within the industry are up against corporate logics that might prevent them from implementing their work or from achieving intended results. The three main corporate and industry logics that the authors examine are meritocracy, technological solutionism, and market fundamentalism. 

Meritocracy is an ideological framework that legitimizes unequal distributions of wealth and power as arising from differences in individual abilities. This has defined the modern subject: as autonomous and responsible for perpetual self-improvement. The tech industry was founded on the myth that it is a meritocratic segment where talents should be rewarded handsomely. This meritocratic belief manifests in the idea that engineers are best at solving ethical issues that their products might create. Similarly, meritocratic logics place a strong emphasis on individual ethics rather than regulation and legislation. Companies and teams try to come up with their own codes of ethics to drive off legislation. The authors conclude that despite their best efforts, ethics owners’ perspectives on larger societal problems are partial, as are their roles within the industry. 

Technological solutionism is the belief that technology can solve social problems, which are then reinforced by the financial rewards that the industry has gained for producing technology that they believe solve the problems. Critics have pointed out that many so-called “solutions” can actually cause problems such as rising income and housing inequalities. The tech industry often responds by proposing even more technical solutions. Similarly, ethical problems are also framed as could be solved by technological solutions. This logic leads to creation of checklists, procedures or evaluative metrics to ensure the design and implementation of ethical products. The authors however point out that this approach is limited, and problematic because it centers ethics in the practices of technologists, and not in the social worlds wherein technical systems are created. 

Market Fundamentalism, or market logics, refers to the idea that companies are there to make money, and if ethics initiatives are cut into the bottom line, companies should not do it. Besides, there is a belief that ethical initiatives are often costly, and antithetical to corporate profits. Furthermore across the industry, if other companies do not implement similar ethical considerations on their products, one should not do it. In the context of the absence of a legal framework, implementing ethics initiatives might be a business problem rather than a solution. In other words, the works of ethics owners in practice are constrained by what the market can allow. 

These three different corporate logics reinforce each other and create a dynamic in which ethics owners have to navigate. Operating within these different logics can create scenarios which the authors termed “normalizing ethical mishaps,” and “blinkered isomorphism.” Normalizing ethical mishaps refers to situations when tech companies create structures that “normalize ethical transgressions;” while “blinkered isomorphism” refer to the process whereby tech companies converge to one structure by learning from each other’s extreme cases while overlooking everyday ethical failings. 

The article sheds light to the current events around the departure of Dr. Gebru from Google. The research shows ethics owners such as Dr. Gebru have to negotiate different competing logics between corporate interests, personal, and professional commitments. The recent events seem to suggest that when corporate logics appear to be more important, ethics owners regardless of how prominent they are could be let go. In other words, “ethics owners” occupy both ambivalent and precarious positions within the tech industry hierarchy.


Original paper by Jacob Metcalf, Emmanuel Moss, and danah boyd: https://datasociety.net/wp-content/uploads/2019/09/Owning-Ethics-PDF-version-2.pdf

Want quick summaries of the latest research & reporting in AI ethics delivered to your inbox? Subscribe to the AI Ethics Brief. We publish bi-weekly.

Primary Sidebar

🔍 SEARCH

Spotlight

Beyond Dependency: The Hidden Risk of Social Comparison in Chatbot Companionship

AI Policy Corner: Restriction vs. Regulation: Comparing State Approaches to AI Mental Health Legislation

Beyond Consultation: Building Inclusive AI Governance for Canada’s Democratic Future

AI Policy Corner: U.S. Executive Order on Advancing AI Education for American Youth

AI Policy Corner: U.S. Copyright Guidance on Works Created with AI

related posts

  • Is the Human Being Lost in the Hiring Process?

    Is the Human Being Lost in the Hiring Process?

  • The Ethics of AI Value Chains: An Approach for Integrating and Expanding AI Ethics Research, Practic...

    The Ethics of AI Value Chains: An Approach for Integrating and Expanding AI Ethics Research, Practic...

  • Towards Sustainable Conversational AI

    Towards Sustainable Conversational AI

  • Mapping the Responsible AI Profession, A Field in Formation (techUK)

    Mapping the Responsible AI Profession, A Field in Formation (techUK)

  • Learning to Prompt in the Classroom to Understand AI Limits: A pilot study

    Learning to Prompt in the Classroom to Understand AI Limits: A pilot study

  • The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks (Research Summa...

    The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks (Research Summa...

  • Who is afraid of black box algorithms? On the epistemological and ethical basis of trust in medical ...

    Who is afraid of black box algorithms? On the epistemological and ethical basis of trust in medical ...

  • Research summary: A Case for Humans-in-the-Loop: Decisions in the Presence of Erroneous Algorithmic ...

    Research summary: A Case for Humans-in-the-Loop: Decisions in the Presence of Erroneous Algorithmic ...

  • Exchanging Lessons Between Algorithmic Fairness and Domain Generalization (Research Summary)

    Exchanging Lessons Between Algorithmic Fairness and Domain Generalization (Research Summary)

  • Risk of AI in Healthcare: A Study Framework

    Risk of AI in Healthcare: A Study Framework

Partners

  •  
    U.S. Artificial Intelligence Safety Institute Consortium (AISIC) at NIST

  • Partnership on AI

  • The LF AI & Data Foundation

  • The AI Alliance

Footer

Categories


• Blog
• Research Summaries
• Columns
• Core Principles of Responsible AI
• Special Topics

Signature Content


• The State Of AI Ethics

• The Living Dictionary

• The AI Ethics Brief

Learn More


• About

• Open Access Policy

• Contributions Policy

• Editorial Stance on AI Tools

• Press

• Donate

• Contact

The AI Ethics Brief (bi-weekly newsletter)

About Us


Founded in 2018, the Montreal AI Ethics Institute (MAIEI) is an international non-profit organization equipping citizens concerned about artificial intelligence and its impact on society to take action.


Archive

  • © 2025 MONTREAL AI ETHICS INSTITUTE.
  • This work is licensed under a Creative Commons Attribution 4.0 International License.
  • Learn more about our open access policy here.
  • Creative Commons License

    Save hours of work and stay on top of Responsible AI research and reporting with our bi-weekly email newsletter.