# Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics

arXiv: https://arxiv.org/abs/2202.08792 · HTML: https://mlsanity.com/paper/2202.08792/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 101 references that are in the map.
- First posted on arXiv 17 Feb 2022.

## Builds on (most cited first, 16 of 16)

- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [Measurement and Fairness](https://mlsanity.com/paper/1912.05511/) (2019)
- [Improving fairness in machine learning systems: What do industry practitioners need?](https://mlsanity.com/paper/1812.05239/) (2018)
- [Participation is not a Design Fix for Machine Learning](https://mlsanity.com/paper/2007.02423/) (2020)
- [Re-imagining Algorithmic Fairness in India and Beyond](https://mlsanity.com/paper/2101.09995/) (2021)
- [Problem Formulation and Fairness](https://mlsanity.com/paper/1901.02547/) (2019)
- [Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Support](https://mlsanity.com/paper/2112.05675/) (2021)
- [FairSight: Visual Analytics for Fairness in Decision Making](https://mlsanity.com/paper/1908.00176/) (2019)
- [Everyday algorithm auditing: Understanding the power of everyday users in surfacing harmful algorithmic behaviors](https://mlsanity.com/paper/2105.02980/) (2021)
- [Soliciting Stakeholders' Fairness Notions in Child Maltreatment Predictive Systems](https://mlsanity.com/paper/2102.01196/) (2021)
- [Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and Stir"](https://mlsanity.com/paper/2111.01122/) (2021)
- [Principles to Practices for Responsible AI: Closing the Gap](https://mlsanity.com/paper/2006.04707/) (2020)
- [Value Cards: An Educational Toolkit for Teaching Social Impacts of Machine Learning through Deliberation](https://mlsanity.com/paper/2010.11411/) (2020)
- [Data Science as Political Action: Grounding Data Science in a Politics of Justice](https://mlsanity.com/paper/1811.03435/) (2018)

## Built on by (most cited first, 28 of 28)

- [Farsight: Fostering Responsible AI Awareness During AI Application Prototyping](https://mlsanity.com/paper/2402.15350/) (2024)
- [From plane crashes to algorithmic harm: applicability of safety engineering frameworks for responsible ML](https://mlsanity.com/paper/2210.03535/) (2022)
- [The four-fifths rule is not disparate impact: a woeful tale of epistemic trespassing in algorithmic fairness](https://mlsanity.com/paper/2202.09519/) (2022)
- [Position: Towards Bidirectional Human-AI Alignment](https://mlsanity.com/paper/2406.09264/) (2024)
- [Investigating Practices and Opportunities for Cross-functional Collaboration around AI Fairness in Industry Practice](https://mlsanity.com/paper/2306.06542/) (2023)
- [AI Mismatches: Identifying Potential Algorithmic Harms Before AI Development](https://mlsanity.com/paper/2502.18682/) (2025)
- [Responsible AI Research Needs Impact Statements Too](https://mlsanity.com/paper/2311.11776/) (2023)
- [A Systematic Literature Review of Human-Centered, Ethical, and Responsible AI](https://mlsanity.com/paper/2302.05284/) (2023)
- [Studying Up Public Sector AI: How Networks of Power Relations Shape Agency Decisions Around AI Design and Use](https://mlsanity.com/paper/2405.12458/) (2024)
- [Law and the Emerging Political Economy of Algorithmic Audits](https://mlsanity.com/paper/2406.11855/) (2024)
- [A Toolkit for Compliance, a Toolkit for Justice: Drawing on Cross-sectoral Expertise to Develop a Pro-justice EU AI Act Toolkit](https://mlsanity.com/paper/2505.17165/) (2025)
- [The Value-Sensitive Conversational Agent Co-Design Framework](https://mlsanity.com/paper/2310.11848/) (2023)
- [Data Ethics Emergency Drill: A Toolbox for Discussing Responsible AI for Industry Teams](https://mlsanity.com/paper/2403.10438/) (2024)
- [Thoughtful Adoption of NLP for Civic Participation: Understanding Differences Among Policymakers](https://mlsanity.com/paper/2410.22937/) (2024)
- [Legacy Procurement Practices Shape How U.S. Cities Govern AI: Understanding Government Employees' Practices, Challenges, and Needs](https://mlsanity.com/paper/2411.04994/) (2024)
- [Talking About the Assumption in the Room](https://mlsanity.com/paper/2502.13268/) (2025)
- [Co-Producing AI: Toward an Augmented, Participatory Lifecycle](https://mlsanity.com/paper/2508.00138/) (2025)
- [Positive AI: Key Challenges in Designing Artificial Intelligence for Wellbeing](https://mlsanity.com/paper/2304.12241/) (2023)
- [Ethics Whitepaper: Whitepaper on Ethical Research into Large Language Models](https://mlsanity.com/paper/2410.19812/) (2024)
- [AI LEGO: Scaffolding Cross-Functional Collaboration in Industrial Responsible AI Practices during Early Design Stages](https://mlsanity.com/paper/2505.10300/) (2025)
- [What is Safety? Corporate Discourse, Power, and the Politics of Generative AI Safety](https://mlsanity.com/paper/2602.06981/) (2026)
- [From Reflection to Repair: A Scoping Review of Dataset Documentation Tools](https://mlsanity.com/paper/2602.15968/) (2026)
- [Trustworthy AI Posture (TAIP): A Framework for Continuous AI Assurance of Agentic Systems at Horizontal and Vertical scale](https://mlsanity.com/paper/2603.03340/) (2026)
- [To Build or Not to Build? Factors that Lead to Non-Development or Abandonment of AI Systems](https://mlsanity.com/paper/2604.28053/) (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)
- [Rethinking AI Evaluation in Education: The TEACH-AI Framework and Benchmark for Generative AI Assistants](https://mlsanity.com/paper/2512.04107/) (2025)
- [Compliant But Unsatisfactory: The Gap Between Auditing Standards and Practices for Probabilistic Genotyping Software](https://mlsanity.com/paper/2604.10875/) (2026)
- [Design Concept: Scaffolding Geopolitical Reflection Among Tech Workers](https://mlsanity.com/paper/2607.28904/) (2026)

## Abstract (arXiv)

Numerous toolkits have been developed to support ethical AI development. However, toolkits, like all tools, encode assumptions in their design about what work should be done and how. In this paper, we conduct a qualitative analysis of 27 AI ethics toolkits to critically examine how the work of ethics is imagined and how it is supported by these toolkits. Specifically, we examine the discourses toolkits rely on when talking about ethical issues, who they imagine should do the work of ethics, and how they envision the work practices involved in addressing ethics. Among the toolkits, we identify a mismatch between the imagined work of ethics and the support the toolkits provide for doing that work. In particular, we identify a lack of guidance around how to navigate labor, organizational, and institutional power dynamics as they relate to performing ethical work. We use these omissions to chart future work for researchers and designers of AI ethics toolkits.