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Cited by 111Ethics and Social Impacts of AIRacial and Ethnic Identity ResearchDiscrimination and Equality Law

Racial categories in machine learning

Open access
Benthall, Sebastian, Haynes, Bruce D.·2019·Proceedings of the Conference on Fairness, Accountability, and Transparency
DOI 10.1145/3287560.3287575
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Abstract

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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  • Algorithmic fairness research has been largely silent on the issues of racial statistics and categories, and in that silence has thus reified them.

    “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]

  • The use of racial categories in algorithmic fairness research has largely gone unquestioned.

    “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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Research works printed but not linked: 5

  1. [6][6] Pedro Bordalo, Katherine Coffman, Nicola Gennaioli, and Andrei Shleifer. 2016. Stereotypes. The Quarterly Journal of Economics 131, 4 (2016), 1753-1794. -
  2. [7][7] Pierre Bourdieu. 2011. The forms of capital.(1986). Cultural theory: An anthology 1 (2011), 81-93. -
  3. [15][15] Amit Datta, Michael Carl Tschantz, and Anupam Datta. 2015. Automated experiments on ad privacy settings. Proceedings on Privacy Enhancing Technologies 2015, 1 (2015), 92-112. -
  4. [34][34] Bruce G Link and Jo C Phelan. 2001. Conceptualizing stigma. Annual review of Sociology 27, 1 (2001), 363-385. -
  5. [38][38] Ellis P Monk Jr. 2014. Skin tone stratification among black Americans, 20012003. Social Forces 92, 4 (2014), 1313-1337. -

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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
DOI
10.1145/3287560.3287575
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 · 2013: 1
FWCI
13.30
Type
conference paper
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
  • ACM Conference on Fairness, Accountability, and Transparency (FAccT) (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Social Sciences › Safety Research › 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

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