Are “Intersectionally Fair” AI Algorithms Really Fair to Women of Color? A Philosophical Analysis
Abstract
A growing number of studies on fairness in artificial intelligence (AI) use the notion of intersectionality to measure AI fairness. Most of these studies take intersectional fairness to be a matter of statistical parity among intersectional subgroups: an AI algorithm is “intersectionally fair” if the probability of the outcome is roughly the same across all subgroups defined by different combinations of the protected attributes. This paper identifies and examines three fundamental problems with this dominant interpretation of intersectional fairness in AI. First, the dominant approach is so preoccupied with the intersection of attributes/categories (e.g., race, gender) that it fails to address the intersection of oppression (e.g., racism, sexism), which is more central to intersectionality as a critical framework. Second, the dominant approach faces a dilemma between infinite regress and fairness gerrymandering: it either keeps splitting groups into smaller subgroups or arbitrarily selects protected groups. Lastly, the dominant view fails to capture what it really means for AI algorithms to be fair, in terms of both distributive and non-distributive fairness. I distinguish a strong sense of AI fairness from a weak sense that is prevalent in the literature, and conclude by envisioning paths towards strong intersectional fairness in AI.
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Meta-learning involves mastering optimization process and is critical in situations with limited data availability, ensuring models can quickly adapt and perform across diverse tasks.
“This technique involves mastering the optimization process and is critical in situations with limited data availability, to ensure models can quickly adapt and perform across diverse tasks, essential in the current data-driven landscape [253], [254].”
From From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape (2025) · cites this paper as [253]
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McIntosh, T. R., Susnjak, T., Liu, T., Watters, P., Xu, D., Liu, D., & Halgamuge, M. N. (2025) · Technologies · doi:10.3390/technologies13020051 · Cited by 102 · see citing works
“A significant challenge in this area is dealing with intersectional biases [221], [222], [223] and understanding the causal interactions that may contribute to these biases [224], [225], [226], [227].”
Background
- Authors
- Kong, Y.
- Affiliations
- Oregon State University (US)
- Corresponding author
- Kong, Youjin
- ORCID
- Kong, Youjin
- Venue
- 2022 ACM Conference on Fairness Accountability and Transparency
- Publisher
- ACM
- Published
- 2022-06-20
- Pages
- 485-494
- OpenAlex
- W4283170908
- Cited by
- 58
- References
- 29
- Citations by year
- 2026: 12 · 2025: 15 · 2024: 15 · 2023: 15 · 2022: 1
- FWCI
- 11.43
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM Conference on Fairness, Accountability, and Transparency (FAccT) (publishedVersion)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Law › Discrimination and Equality Law
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- intersectional fairness, algorithmic fairness, intersectionality, statistical parity, women of color, protected characteristics, infinite regress, distributive fairness, algorithmic discrimination
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