References
Banja, J., Hilbert, A., & Navathe, A. S. (2023). An American perspective on fairness in model development. PLOS Digital Health, 2(4), e0000386. https://doi.org/10.1371/journal.pdig.0000386
Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and machine learning: Limitations and opportunities. MIT Press. https://doi.org/10.7551/mitpress/11294.001.0001
Bertsimas, D., Farias, V. F., & Trichakis, N. (2011). The price of fairness. Operations Research, 59(1), 17–31. https://doi.org/10.1287/opre.1100.0865
Braveman, P., & Gottlieb, L. (2014). The social determinants of health: It's time to consider the causes of the causes. Public Health Reports, 129(Suppl. 2), 19–31. https://doi.org/10.1177/00333549141291S206
Chen, I. Y., Johansson, F. D., & Sontag, D. (2018). Why is my classifier discriminatory? Advances in Neural Information Processing Systems, 31, 3539–3550. https://doi.org/10.48550/arXiv.1805.12002
Chen, R. J., Wang, J. J., Williamson, D. F. K., Chen, T. Y., Lipkova, J., Lu, M. Y., Sahai, S., & Mahmood, F. (2023). Algorithmic fairness in artificial intelligence for medicine and healthcare. Nature Biomedical Engineering, 7(6), 719–742. https://doi.org/10.1038/s41551-023-01056-8
Chouldechova, A. (2017). Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big Data, 5(2), 153–163. https://doi.org/10.1089/big.2016.0047
Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2009). Introduction to algorithms (3rd ed.). MIT Press. https://doi.org/10.7551/mitpress/9436.001.0001
DeCamp, M., & Lindvall, C. (2020). Latent bias and the implementation of artificial intelligence in medicine. Journal of the American Medical Informatics Association, 27(12), 2020–2023. https://doi.org/10.1093/jamia/ocaa094
Delage, E., & Ye, Y. (2010). Distributionally robust optimization under moment uncertainty with application to data-driven problems. Operations Research, 58(3), 595–612. https://doi.org/10.1287/opre.1090.0741
Hernán, M. A., & Robins, J. M. (2020). Causal inference: What if. Chapman & Hall/CRC.
Kashyap, A., Rostamzadeh, N., & Weiss, J. C. (2026). A pipeline for enabling path-specific causal fairness in observational health data. Proceedings of the ACM Conference on Fairness, Accountability, and Transparency (FAccT). Manuscript accepted for publication.
Kearns, M., Neel, S., Roth, A., & Wu, Z. S. (2018). Preventing fairness gerrymandering: Auditing and learning for subgroup fairness. Proceedings of the 35th International Conference on Machine Learning (ICML), 80, 2564–2572. https://doi.org/10.48550/arXiv.1711.05144
Kino, S., Hsu, Y.-T., Shiba, K., Chung, Y., Núñez, A., Mahalingaiah, S., & Laden, F. (2021). A scoping review on the use of machine learning in research on social determinants of health: Trends and research gaps. SSM – Population Health, 15, 100836. https://doi.org/10.1016/j.ssmph.2021.100836
Kusner, M. J., Loftus, J., Russell, C., & Silva, R. (2017). Counterfactual fairness. Advances in Neural Information Processing Systems, 30, 4066–4076. https://doi.org/10.48550/arXiv.1703.06856
Liu, M., Ning, Y., Teixayavong, S., Mertens, M., Xu, J., Tong, Y. X., Ting, D. S. W., & Celi, L. A. (2025). A scoping review and evidence gap analysis of clinical AI fairness: Toward equitable and trustworthy algorithms. npj Digital Medicine, 8, Article 118. https://doi.org/10.1038/s41746-025-01667-2
Makhlouf, K., Zhioua, S., & Palamidessi, C. (2020). Survey on causal-based machine learning fairness notions. arXiv. https://doi.org/10.48550/arXiv.2010.09553
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), Article 115. https://doi.org/10.1145/3457607
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453. https://doi.org/10.1126/science.aax2342
Pearl, J. (2009). Causality: Models, reasoning, and inference (2nd ed.). Cambridge University Press. https://doi.org/10.1017/CBO9780511803161
Periáñez, Á., Höhl, A., Venot, Q., & Lepetit, P. (2024). The digital transformation in health: How AI can improve the performance of health systems. Health Systems & Reform, 10(1), Article e2387138. https://doi.org/10.1080/23288604.2024.2387138
Rajkomar, A., Hardt, M., Howell, M. D., Corrado, G., & Chin, M. H. (2018). Ensuring fairness in machine learning to advance health equity. Annals of Internal Medicine, 169(12), 866–872. https://doi.org/10.7326/M18-1990
Shang, T., Chen, S., & Xu, H. (2024). Integrating social determinants of health into knowledge graphs: Evaluating prediction bias and fairness in healthcare. arXiv. https://doi.org/10.48550/arXiv.2412.00245
Vyas, D. A., Eisenstein, L. G., & Jones, D. S. (2020). Hidden in plain sight — reconsidering the use of race correction in clinical algorithms. New England Journal of Medicine, 383(9), 874–882. https://doi.org/10.1056/NEJMms2004740
Wiens, J., Saria, S., Sendak, M., Ghassemi, M., Liu, V. X., Doshi-Velez, F., Jung, K., Heller, K., Kale, D., Saeed, M., Ossorio, P. N., Thadaney-Israni, S., & Goldenberg, A. (2019). Do no harm: A roadmap for responsible machine learning for health care. Nature Medicine, 25(9), 1337–1340. https://doi.org/10.1038/s41591-019-0548-6