# Epistemic Power in AI Ethics Labor: Legitimizing Located Complaints

arXiv: https://arxiv.org/abs/2402.08171 · HTML: https://mlsanity.com/paper/2402.08171/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2024.

## Built on by (most cited first, 6 of 6)

- [Acceptable Use Policies for Foundation Models](https://mlsanity.com/paper/2409.09041/) (2024)
- [Ethics Whitepaper: Whitepaper on Ethical Research into Large Language Models](https://mlsanity.com/paper/2410.19812/) (2024)
- [Dual-Metric Evaluation of Social Bias in Large Language Models: Evidence from an Underrepresented Nepali Cultural Context](https://mlsanity.com/paper/2603.07792/) (2026)
- [When Algorithms Meet Artists: Semantic Compression and Stake-holder Marginalisation in Public AI-Art Discourse (2013-2025)](https://mlsanity.com/paper/2508.03037/) (2025)
- [How Formerly Incarcerated People Envision Technologies for Prison Parole](https://mlsanity.com/paper/2607.16513/) (2026)
- [Abstracted Away: Resisting Alienation and Ungrounded Abstraction in AI Research Communities](https://mlsanity.com/paper/2608.08408/) (2026)

## Abstract (arXiv)

What counts as legitimate AI ethics labor, and consequently, what are the epistemic terms on which AI ethics claims are rendered legitimate? Based on 75 interviews with technologists including researchers, developers, open source contributors, and activists, this paper explores the various epistemic bases from which AI ethics is discussed and practiced. In the context of outside attacks on AI ethics as an impediment to "progress," I show how some AI ethics practices have reached toward authority from automation and quantification, and achieved some legitimacy as a result, while those based on richly embodied and situated lived experience have not. This paper draws together the work of feminist Anthropology and Science and Technology Studies scholars Diana Forsythe and Lucy Suchman with the works of postcolonial feminist theorist Sara Ahmed and Black feminist theorist Kristie Dotson to examine the implications of dominant AI ethics practices. By entrenching the epistemic power of quantification, dominant AI ethics practices -- employing Model Cards and similar interventions -- risk legitimizing AI ethics as a project in equal and opposite measure to which they marginalize embodied lived experience as a legitimate part of the same project. In response, I propose humble technical practices: quantified or technical practices which specifically seek to make their epistemic limits clear in order to flatten hierarchies of epistemic power.