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DIGITAL HEALTH

Diagnosing bias in data-driven algorithms for healthcare

Nature Medicine volume 26, pages 25–26 (2020)Cite this article

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A recent analysis highlighting the potential for algorithms to perpetuate existing racial biases in healthcare underscores the importance of thinking carefully about the labels used during algorithm development.

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Author information

Authors and Affiliations

  1. Division of Computer Science and Engineering, University of Michigan, Ann Arbor, MI, USA

    Jenna Wiens

  2. Law School, University of Michigan, Ann Arbor, MI, USA

    W. Nicholson Price II

  3. Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA

    Michael W. Sjoding

Authors
  1. Jenna Wiens
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  2. W. Nicholson Price II
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  3. Michael W. Sjoding
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Corresponding author

Correspondence to Jenna Wiens.

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The authors declare no competing interests.

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Wiens, J., Price, W.N. & Sjoding, M.W. Diagnosing bias in data-driven algorithms for healthcare. Nat Med 26, 25–26 (2020). https://doi.org/10.1038/s41591-019-0726-6

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