# Walking the Walk of AI Ethics: Organizational Challenges and the Individualization of Risk among Ethics Entrepreneurs

arXiv: https://arxiv.org/abs/2305.09573 · HTML: https://mlsanity.com/paper/2305.09573/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 43 references that are in the map.
- First posted on arXiv 16 May 2023.

## Builds on (most cited first, 2 of 2)

- [Improving fairness in machine learning systems: What do industry practitioners need?](https://mlsanity.com/paper/1812.05239/) (2018)
- [Principles alone cannot guarantee ethical AI](https://mlsanity.com/paper/1906.06668/) (2019)

## Built on by (most cited first, 12 of 12)

- [Farsight: Fostering Responsible AI Awareness During AI Application Prototyping](https://mlsanity.com/paper/2402.15350/) (2024)
- [Responsible AI Research Needs Impact Statements Too](https://mlsanity.com/paper/2311.11776/) (2023)
- [Privy: Envisioning and Mitigating Privacy Risks for Consumer-facing AI Product Concepts](https://mlsanity.com/paper/2509.23525/) (2025)
- [The Value-Sensitive Conversational Agent Co-Design Framework](https://mlsanity.com/paper/2310.11848/) (2023)
- [A Conceptual Framework for Ethical Evaluation of Machine Learning Systems](https://mlsanity.com/paper/2408.10239/) (2024)
- [When AI Fails, What Works? A Data-Driven Taxonomy of Real-World AI Risk Mitigation Strategies](https://mlsanity.com/paper/2603.04259/) (2026)
- [Engaged AI Governance: Addressing the Last Mile Challenge Through Internal Expert Collaboration](https://mlsanity.com/paper/2604.21554/) (2026)
- ["Accessibility people, you go work on that thing of yours over there": Addressing Disability Inclusion in AI Product Organizations](https://mlsanity.com/paper/2508.16607/) (2025)
- [From Reflection to Repair: A Scoping Review of Dataset Documentation Tools](https://mlsanity.com/paper/2602.15968/) (2026)
- [Fairness-in-the-Workflow: How Machine Learning Practitioners at Big Tech Companies Approach Fairness in Recommender Systems](https://mlsanity.com/paper/2505.19441/) (2025)
- [Post-Deployment Accountability in AI Governance: A Cross-Regulatory Empirical Analysis of AI Incidents](https://mlsanity.com/paper/2605.16281/) (2026)
- [What We Know about Responsible AI Practices in Industry: A Half Decade of Empirical Research](https://mlsanity.com/paper/2608.10431/) (2026)

## Abstract (arXiv)

Amidst decline in public trust in technology, computing ethics have taken center stage, and critics have raised questions about corporate ethics washing. Yet few studies examine the actual implementation of AI ethics values in technology companies. Based on a qualitative analysis of technology workers tasked with integrating AI ethics into product development, we find that workers experience an environment where policies, practices, and outcomes are decoupled. We analyze AI ethics workers as ethics entrepreneurs who work to institutionalize new ethics-related practices within organizations. We show that ethics entrepreneurs face three major barriers to their work. First, they struggle to have ethics prioritized in an environment centered around software product launches. Second, ethics are difficult to quantify in a context where company goals are incentivized by metrics. Third, the frequent reorganization of teams makes it difficult to access knowledge and maintain relationships central to their work. Consequently, individuals take on great personal risk when raising ethics issues, especially when they come from marginalized backgrounds. These findings shed light on complex dynamics of institutional change at technology companies.