# (Unfair) Norms in Fairness Research: A Meta-Analysis

arXiv: https://arxiv.org/abs/2407.16895 · HTML: https://mlsanity.com/paper/2407.16895/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 90 references that are in the map.
- First posted on arXiv 17 Jun 2024.

## Builds on (most cited first, 18 of 18)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Power to the People? Opportunities and Challenges for Participatory AI](https://mlsanity.com/paper/2209.07572/) (2022)
- [On the Apparent Conflict Between Individual and Group Fairness](https://mlsanity.com/paper/1912.06883/) (2019)
- [Re-imagining Algorithmic Fairness in India and Beyond](https://mlsanity.com/paper/2101.09995/) (2021)
- [Reduced, Reused and Recycled: The Life of a Dataset in Machine Learning Research](https://mlsanity.com/paper/2112.01716/) (2021)
- [Fairness for Unobserved Characteristics: Insights from Technological Impacts on Queer Communities](https://mlsanity.com/paper/2102.04257/) (2021)
- [The Forgotten Margins of AI Ethics](https://mlsanity.com/paper/2205.04221/) (2022)
- [WEIRD FAccTs: How Western, Educated, Industrialized, Rich, and Democratic is FAccT?](https://mlsanity.com/paper/2305.06415/) (2023)
- [A Sociotechnical View of Algorithmic Fairness](https://mlsanity.com/paper/2110.09253/) (2021)
- [Global AI Ethics: A Review of the Social Impacts and Ethical Implications of Artificial Intelligence](https://mlsanity.com/paper/1907.07892/) (2019)
- ["There Is Not Enough Information": On the Effects of Explanations on Perceptions of Informational Fairness and Trustworthiness in Automated Decision-Making](https://mlsanity.com/paper/2205.05758/) (2022)
- [Extending the Machine Learning Abstraction Boundary: A Complex Systems Approach to Incorporate Societal Context](https://mlsanity.com/paper/2006.09663/) (2020)
- [Four Years of FAccT: A Reflexive, Mixed-Methods Analysis of Research Contributions, Shortcomings, and Future Prospects](https://mlsanity.com/paper/2206.06738/) (2022)
- [Subverting machines, fluctuating identities: Re-learning human categorization](https://mlsanity.com/paper/2205.13740/) (2022)
- [An Empirical Analysis of Racial Categories in the Algorithmic Fairness Literature](https://mlsanity.com/paper/2309.06607/) (2023)
- [Fast online ranking with fairness of exposure](https://mlsanity.com/paper/2209.13019/) (2022)
- [Fast flow-based algorithm for creating density-equalizing map projections](https://mlsanity.com/paper/1802.07625/) (2018)
- [Fairness Vs. Personalization: Towards Equity in Epistemic Utility](https://mlsanity.com/paper/2309.11503/) (2023)

## Built on by (most cited first, 1 of 1)

- [When Stereotypes GTG: The Impact of Predictive Text Suggestions on Gender Bias in Human-AI Co-Writing](https://mlsanity.com/paper/2409.20390/) (2024)

## Abstract (arXiv)

Algorithmic fairness has emerged as a critical concern in artificial intelligence (AI) research. However, the development of fair AI systems is not an objective process. Fairness is an inherently subjective concept, shaped by the values, experiences, and identities of those involved in research and development. To better understand the norms and values embedded in current fairness research, we conduct a meta-analysis of algorithmic fairness papers from two leading conferences on AI fairness and ethics, AIES and FAccT, covering a final sample of 139 papers over the period from 2018 to 2022. Our investigation reveals two concerning trends: first, a US-centric perspective dominates throughout fairness research; and second, fairness studies exhibit a widespread reliance on binary codifications of human identity (e.g., "Black/White", "male/female"). These findings highlight how current research often overlooks the complexities of identity and lived experiences, ultimately failing to represent diverse global contexts when defining algorithmic bias and fairness. We discuss the limitations of these research design choices and offer recommendations for fostering more inclusive and representative approaches to fairness in AI systems, urging a paradigm shift that embraces nuanced, global understandings of human identity and values.