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Racial categories in machine learning
Open accessAbstract
Controversies around race and machine learning have sparked debate among computer scientists over how to design machine learning systems that guarantee fairness. These debates rarely engage with how racial identity is embedded in our social experience, making for sociological and psychological complexity. This complexity challenges the paradigm of considering fairness to be a formal property of supervised learning with respect to protected personal attributes. Racial identity is not simply a personal subjective quality. For people labeled "Black" it is an ascribed political category that has consequences for social differentiation embedded in systemic patterns of social inequality achieved through both social and spatial segregation. In the United States, racial classification can best be understood as a system of inherently unequal status categories that places whites as the most privileged category while signifying the Negro/black category as stigmatized. Social stigma is reinforced through the unequal distribution of societal rewards and goods along racial lines that is reinforced by state, corporate, and civic institutions and practices. This creates a dilemma for society and designers: be blind to racial group disparities and thereby reify racialized social inequality by no longer measuring systemic inequality, or be conscious of racial categories in a way that itself reifies race. We propose a third option. By preceding group fairness interventions with unsupervised learning to dynamically detect patterns of segregation, machine learning systems can mitigate the root cause of social disparities, social segregation and stratification, without further anchoring status categories of disadvantage.
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“We wholly agree with their overall argument, which is that algorithmic fairness research has been largely silent on the issues of racial statistics and categories, and that in that silence, has thus reified them.”
From Towards a critical race methodology in algorithmic fairness (2020) · cites this paper as [12]
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“While an area of major concern for critical race and technology scholarship, the use of racial categories in algorithmic fairness research (i.e. the research community which has emerged around venues like FA T* and AIES) has largely gone unquestioned.”
From Towards a critical race methodology in algorithmic fairness (2020) · cites this paper as [12]
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Metadata
- Authors
- Benthall, S., & Haynes, B. D.
- Affiliations
- New York University (US)
- University of California, Davis (US)
- ORCID
- Benthall, Sebastian
- Venue
- Proceedings of the Conference on Fairness, Accountability, and Transparency
- Publisher
- ACM
- Published
- 2019-01-29
- Pages
- 289-298
- OpenAlex
- W2902633481
- Cited by
- 110
- References
- 56
- Citations by year
- 2026: 7 · 2025: 17 · 2024: 11 · 2023: 23 · 2022: 11 · 2021: 19 · 2020: 16 · 2019: 5
- FWCI
- 13.30
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ACM Conference on Fairness, Accountability, and Transparency (FAccT) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Sociology and Political Science › Racial and Ethnic Identity Research
- Social Sciences › Law › Discrimination and Equality Law
- Keywords
- racial classification, algorithmic fairness, social segregation, social stratification, unsupervised learning, racial inequality, Black identity, social stigma, protected characteristics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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