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Translating intersectionality to fair machine learning in health sciences

Nature Machine Intelligence volume 5, pages 476–479 (2023)Cite this article

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Fairness approaches in machine learning should involve more than an assessment of performance metrics across groups. Shifting the focus away from model metrics, we reframe fairness through the lens of intersectionality, a Black feminist theoretical framework that contextualizes individuals in interacting systems of power and oppression.

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Acknowledgements

E.L. thanks L. Bowleg and G. Bauer from the Intersectionality Training Institute and the E2 Social Epidemiology Lab for their support of this work. The authors thank M. Roberson and K. Baker for their feedback on the manuscript. E.L. was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) grant 5 T32 HD40128-19. W.G.L. was supported by the National Institutes of Health (NIH) National Library of Medicine grant R00-LM012926.

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Authors and Affiliations

  1. Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA, USA

    Elle Lett & William G. La Cava

  2. Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Elle Lett

  3. Center for Applied Transgender Studies, Chicago, IL, USA

    Elle Lett

  4. Harvard Medical School, Boston, MA, USA

    William G. La Cava

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  1. Elle Lett
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Correspondence to Elle Lett.

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Nature Machine Intelligence thanks the anonymous reviewers for their contribution to the peer review of this work.

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Lett, E., La Cava, W.G. Translating intersectionality to fair machine learning in health sciences. Nat Mach Intell 5, 476–479 (2023). https://doi.org/10.1038/s42256-023-00651-3

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