Algorithmic Bias
EP. 08
In this episode, we discuss algorithmic bias and fairness in healthcare. We explain what this is, the different definitions of “fairness”, explore the ways in which bias can enter the machine learning pipeline and some ways to combat it.
01:00 Technical update - NeurIPS
06:50 Technical update - ChatGPT-o3
14:00 - Algorithmic bias
Some resources and papers we discuss:
http://www.incompleteideas.net/IncIdeas/BitterLesson.html
https://rodneybrooks.com/a-better-lesson/
Brodeur, P.G., Buckley, T.A., Kanjee, Z., Goh, E., Ling, E.B., Jain, P., Cabral, S., Abdulnour, R., Haimovich, A., Freed, J., Olson, A.P., Morgan, D.J., Hom, J., Gallo, R.J., Horvitz, E., Chen, J., Manrai, A.K., & Rodman, A. (2024). Superhuman performance of a large language model on the reasoning tasks of a physician. <https://arxiv.org/abs/2412.10849>
Chen IY, Pierson E, Rose S, Joshi S, Ferryman K, Ghassemi M. Ethical Machine Learning in Healthcare. Annu Rev Biomed Data Sci. 2021 Jul;4:123-144. doi: 10.1146/annurev-biodatasci-092820-114757. Epub 2021 May 6. PMID: 34396058; PMCID: PMC8362902.<https://pubmed.ncbi.nlm.nih.gov/34396058/>
Chen, R.J., Wang, J.J., Williamson, D.F.K. et al. Algorithmic fairness in artificial intelligence for medicine and healthcare. Nat. Biomed. Eng 7, 719–742 (2023). https://doi.org/10.1038/s41551-023-01056-8
Hooker S, 2021 Moving beyond “algorithmic bias is a data problem”, Patterns <https://www.cell.com/patterns/fulltext/S2666-3899(21)00061-1>
Sagers, L., Diao, J.A., Melas-Kyriazi, L., Groh, M., Rajpurkar, P., Adamson, A.S., Rotemberg, V., Daneshjou, R., & Manrai, A.K. (2023). Augmenting medical image classifiers with synthetic data from latent diffusion models. ArXiv, abs/2308.12453. <https://arxiv.org/abs/2308.12453>
Yang, Y., Zhang, H., Gichoya, J.W. et al. The limits of fair medical imaging AI in real-world generalization. Nat Med 30, 2838–2848 (2024). https://doi.org/10.1038/s41591-024-03113-4
Omiye, J.A., Lester, J.C., Spichak, S. et al. Large language models propagate race-based medicine. npj Digit. Med. 6, 195 (2023). https://doi.org/10.1038/s41746-023-00939-z
Crystal T. Chang, Neha Srivathsa, Charbel Bou-Khalil, Akshay Swaminathan, Mitchell R. Lunn, Kavita Mishra, Roxana Daneshjou, Sanmi Koyejo 2024 Evaluating Anti-LGBTQIA+ Medical Bias in Large Language Models medRxiv 2024.08.22.24312464; doi: https://doi.org/10.1101/2024.08.22.24312464
Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations. Lancet Digit Health. 2025 Jan;7(1):e64-e88. doi: 10.1016/S2589-7500(24)00224-3. Epub 2024 Dec 18. PMID: 39701919; PMCID: PMC11668905. <https://pubmed.ncbi.nlm.nih.gov/39701919/>
Coalition for Healthcare AI (CHAI) Assurance Standards <https://chai.org/wp-content/uploads/2024/07/CHAI-Assurance-Standards-Guide-6-26-2024.pdf>
Chidambaram S, et al An introduction to digital determinants of health. PLOS Digit Health. 2024 Jan 4;3(1):e0000346. doi: 10.1371/journal.pdig.0000346. PMID: 38175828; PMCID: PMC10766177.