Machine learning models are increasingly being deployed in real-world clinical settings and have shown promise in patient diagnosis, treatment and outcome tasks. However, such models have also been shown to exhibit biases towards specific demographic groups, leading to inequitable outcomes for under-represented or historically marginalized communities.
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Acknowledgements
This work was supported in part by a National Science Foundation (NSF) 22-586 Faculty Early Career Development Award (no. 2339381), a Gordon & Betty Moore Foundation award and a Google Research Scholar award.
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Zhang, H., Gerych, W. & Ghassemi, M. A data-centric perspective to fair machine learning for healthcare. Nat Rev Methods Primers 4, 86 (2024). https://doi.org/10.1038/s43586-024-00371-x
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DOI: https://doi.org/10.1038/s43586-024-00371-x
