# What is the Point of Fairness? Disability, AI and The Complexity of Justice

arXiv: https://arxiv.org/abs/1908.01024 · HTML: https://mlsanity.com/paper/1908.01024/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2019.

## Built on by (most cited first, 7 of 7)

- [Computer Vision and Conflicting Values: Describing People with Automated Alt Text](https://mlsanity.com/paper/2105.12754/) (2021)
- [Lazy Data Practices Harm Fairness Research](https://mlsanity.com/paper/2404.17293/) (2024)
- ["Cold, Calculated, and Condescending": How AI Identifies and Explains Ableism Compared to Disabled People](https://mlsanity.com/paper/2410.03448/) (2024)
- [Data Representativeness in Accessibility Datasets: A Meta-Analysis](https://mlsanity.com/paper/2207.08037/) (2022)
- [Axes for Sociotechnical Inquiry in AI Research](https://mlsanity.com/paper/2105.06551/) (2021)
- [Identities are not Interchangeable: The Problem of Overgeneralization in Fair Machine Learning](https://mlsanity.com/paper/2505.04038/) (2025)
- [A Keyword Based Approach to Understanding the Overpenalization of Marginalized Groups by English Marginal Abuse Models on Twitter](https://mlsanity.com/paper/2210.06351/) (2022)

## Abstract (arXiv)

Work integrating conversations around AI and Disability is vital and valued, particularly when done through a lens of fairness. Yet at the same time, analyzing the ethical implications of AI for disabled people solely through the lens of a singular idea of "fairness" risks reinforcing existing power dynamics, either through reinforcing the position of existing medical gatekeepers, or promoting tools and techniques that benefit otherwise-privileged disabled people while harming those who are rendered outliers in multiple ways. In this paper we present two case studies from within computer vision - a subdiscipline of AI focused on training algorithms that can "see" - of technologies putatively intended to help disabled people but, through failures to consider structural injustices in their design, are likely to result in harms not addressed by a "fairness" framing of ethics. Drawing on disability studies and critical data science, we call on researchers into AI ethics and disability to move beyond simplistic notions of fairness, and towards notions of justice.