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Bias in artificial intelligence algorithms and recommendations for mitigation

Author

Listed:
  • Lama H Nazer
  • Razan Zatarah
  • Shai Waldrip
  • Janny Xue Chen Ke
  • Mira Moukheiber
  • Ashish K Khanna
  • Rachel S Hicklen
  • Lama Moukheiber
  • Dana Moukheiber
  • Haobo Ma
  • Piyush Mathur

Abstract

The adoption of artificial intelligence (AI) algorithms is rapidly increasing in healthcare. Such algorithms may be shaped by various factors such as social determinants of health that can influence health outcomes. While AI algorithms have been proposed as a tool to expand the reach of quality healthcare to underserved communities and improve health equity, recent literature has raised concerns about the propagation of biases and healthcare disparities through implementation of these algorithms. Thus, it is critical to understand the sources of bias inherent in AI-based algorithms. This review aims to highlight the potential sources of bias within each step of developing AI algorithms in healthcare, starting from framing the problem, data collection, preprocessing, development, and validation, as well as their full implementation. For each of these steps, we also discuss strategies to mitigate the bias and disparities. A checklist was developed with recommendations for reducing bias during the development and implementation stages. It is important for developers and users of AI-based algorithms to keep these important considerations in mind to advance health equity for all populations.

Suggested Citation

  • Lama H Nazer & Razan Zatarah & Shai Waldrip & Janny Xue Chen Ke & Mira Moukheiber & Ashish K Khanna & Rachel S Hicklen & Lama Moukheiber & Dana Moukheiber & Haobo Ma & Piyush Mathur, 2023. "Bias in artificial intelligence algorithms and recommendations for mitigation," PLOS Digital Health, Public Library of Science, vol. 2(6), pages 1-14, June.
    • Handle: RePEc:plo:pdig00:0000278
    DOI: 10.1371/journal.pdig.0000278
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    References listed on IDEAS

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    1. Nenad Tomašev & Xavier Glorot & Jack W. Rae & Michal Zielinski & Harry Askham & Andre Saraiva & Anne Mottram & Clemens Meyer & Suman Ravuri & Ivan Protsyuk & Alistair Connell & Cían O. Hughes & Alan K, 2019. "A clinically applicable approach to continuous prediction of future acute kidney injury," Nature, Nature, vol. 572(7767), pages 116-119, August.
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    Cited by:

    1. Hamish S Fraser & Alvin Marcelo & Mahima Kalla & Khumbo Kalua & Leo A Celi & Jennifer Ziegler, 2023. "Digital determinants of health: Editorial," PLOS Digital Health, Public Library of Science, vol. 2(11), pages 1-4, November.
    2. Marie-Laure Charpignon & Leo Anthony Celi & Marisa Cobanaj & Rene Eber & Amelia Fiske & Jack Gallifant & Chenyu Li & Gurucharan Lingamallu & Anton Petushkov & Robin Pierce, 2024. "Diversity and inclusion: A hidden additional benefit of Open Data," PLOS Digital Health, Public Library of Science, vol. 3(7), pages 1-17, July.

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