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Designing equitable algorithms

Nature Computational Science volume 3, pages 601–610 (2023)Cite this article

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Abstract

Predictive algorithms are now commonly used to distribute society’s resources and sanctions. But these algorithms can entrench and exacerbate inequities. To guard against this possibility, many have suggested that algorithms be subject to formal fairness constraints. Here we argue, however, that popular constraints—while intuitively appealing—often worsen outcomes for individuals in marginalized groups, and can even leave all groups worse off. We outline a more holistic path forward for improving the equity of algorithmically guided decisions.

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Fig. 1: The consequences of miscalibrated risk scores.
Fig. 2: The distribution of diabetes risk for all patients and patients with diabetes.
Fig. 3: Inherent trade-offs in ride-share allocation arising from the geographic distribution of residents.
Fig. 4: Preferences for allocating ride-share vouchers.
Fig. 5: The impact of label bias on calibration.

Data availability

The data to reproduce our analysis are available at https://github.com/madisoncoots/equitable-algorithms.

Code availability

The code to reproduce our analysis is available at https://github.com/madisoncoots/equitable-algorithms.

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Acknowledgements

We thank S. Corbett-Davies, J. Gaebler, A. Feller, D. Kent, K. Ladin, H. Nilforoshan and R. Shroff for helpful conversations. Our work was supported by grants from the Harvard Data Science Initiative, the Stanford Impact Labs and Stanford Law School.

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  1. Stanford University, Stanford, CA, USA

    Alex Chohlas-Wood & Julian Nyarko

  2. Harvard University, Cambridge, MA, USA

    Madison Coots & Sharad Goel

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  1. Alex Chohlas-Wood
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  2. Madison Coots
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All authors contributed equally to this work.

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Correspondence to Alex Chohlas-Wood, Madison Coots, Sharad Goel or Julian Nyarko.

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Chohlas-Wood, A., Coots, M., Goel, S. et al. Designing equitable algorithms. Nat Comput Sci 3, 601–610 (2023). https://doi.org/10.1038/s43588-023-00485-4

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