fairMLHealth

Healthcare-specific tools for bias analysis

View the Project on GitHub KenSciResearch/fairMLHealth

References and Outside Resources


Academic References

Agniel, D., Kohane, I.S., & Weber, G.M. (2018). Biases in electronic health record data due to processes within the healthcare system: retrospective observational study. Bmj, 361. Retrieved from https://www.bmj.com/content/361/bmj.k1479

Agarwal, A., Beygelzimer, A., Dudík, M., Langford, J., & Wallach, H. (2018). A reductions approach to fair classification. In International Conference on Machine Learning (pp. 60-69). PMLR. Available through arXiv preprint:1803.02453.

Agarwal, A., Beygelzimer, A., Dudík, M., Langford, J., & Wallach, H. (2018). A Reductions Approach to Fair Classification. Retrieved from https://arxiv.org/pdf/1803.02453

Agarwal, A., Dudik, M., & Wu, Z.S. (2019, May). Fair regression: Quantitative definitions and reduction-based algorithms. In International Conference on Machine Learning (pp. 120-129). PMLR. Available through https://arxiv.org/pdf/1905.12843.pdf

Anders, C.J., Pasliev, P., Dombrowski, A.K., Müller, K.R., & Kessel, P. (2020). Fairwashing Explanations with Off-Manifold Detergent. Retrieved from http://arxiv.org/abs/2007.09969

Bantilan, N. (2018). Themis-ml: A fairness-aware machine learning interface for end-to-end discrimination discovery and mitigation. Journal of Technology in Human Services, 36(1), 15-30. Retrieved from https://www.tandfonline.com/doi/abs/10.1080/15228835.2017.1416512

Barocas, S., & Selbst, A.D. (2016). Big data’s disparate impact. California Law Review, 104, 671. Retrieved from https://www.cs.yale.edu/homes/jf/BarocasDisparateImpact.pdf

Bellamy, R.K., Dey, K., Hind, M., Hoffman, S.C., Houde, S., Kannan, K., … & Nagar, S. (2018). AI Fairness 360: An extensible toolkit for detecting, understanding, and mitigating unwanted algorithmic bias. arXiv Preprint. arXiv:1810.01943.. See Also AIF360 Documentation

Berk, R., Heidari, H., Jabbari, S., Joseph, M., Kearns, M., Morgenstern, J., Neel, S. and Roth, A., 2017. A convex framework for fair regression. arXiv preprint retrieved from https://arxiv.org/pdf/1706.02409

de Bie, K., Lucic, A. and Haned, H., 2021. To Trust or Not to Trust a Regressor: Estimating and Explaining Trustworthiness of Regression Predictions. arXiv preprint arXiv:2104.06982. arXiv preprint retrieved from https://arxiv.org/pdf/2104.06982

Bird, S., Dudík, M., Wallach, H., & Walker, K. (2020). Fairlearn: A toolkit for assessing and improving fairness in AI. Microsoft Research. Retrieved from https://www.microsoft.com/en-us/research/uploads/prod/2020/05/Fairlearn_whitepaper.pdf. See Also FairLearn Reference.

Corbett-Davies, S., & Goel, S. (2018). The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning. Retrieved from http://arxiv.org/abs/1808.00023

Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. (2012, January). Fairness through awareness. In Proceedings of the 3rd innovations in theoretical computer science conference (pp. 214-226). Retrieved from https://arxiv.org/pdf/1104.3913.pdf

Equal Employment Opportunity Commission, & Civil Service Commission, Department of Labor & Department of Justice (1978). Uniform guidelines on employee selection procedures. Federal Register, 43(166), 38290-38315. Retrieved from http://uniformguidelines.com/uniformguidelines.html#18

Hardt, M., Price, E., & Srebro, N. (2016). Equality of opportunity in supervised learning. In Advances in neural information processing systems (pp. 3315-3323). Retrieved from http://papers.nips.cc/paper/6374-equality-of-opportunity-in-supervised-learning.pdf

Healthcare Cost and Utilization Project (HCUP) (2017, March). HCUP CCS. Agency for Healthcare Research and Quality, Rockville, MD. Retrieved from www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp

Johnson, A.E.W., Pollard, T.J., Shen, L., Lehman, L., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Celi, L.A., & Mark, R.G. (2016). Scientific Data. MIMIC-III, a freely accessible critical care database. DOI: 10.1038/sdata.2016.35. Retrieved from http://www.nature.com/articles/sdata201635

Kearns, M., Neel, S., Roth, A., & Wu, Z.S. (2018, July). Preventing fairness gerrymandering: Auditing and learning for subgroup fairness. In International Conference on Machine Learning (pp. 2564-2572). PMLR. Available through http://proceedings.mlr.press/v80/kearns18a.html

Kim, M., Reingol, O., & Rothblum, G. (2018). Fairness through computationally-bounded awareness. In Advances in Neural Information Processing Systems (pp. 4842-4852). Retrieved from https://arxiv.org/pdf/1803.03239.pdf

National Association for the Advancement of Colored People (NAACP) (2012). Criminal Justice Fact Sheet. NAACP. Retrieved from https://naacp.org/resources/criminal-justice-fact-sheet.

Romei, A., & Ruggieri, S. (2014). A multidisciplinary survey on discrimination analysis. The Knowledge Engineering Review, 29(5), 582-638. Retrieved from https://www.cambridge.org/core/journals/knowledge-engineering-review/article/multidisciplinary-survey-on-discrimination-analysis/D69E925AC96CDEC643C18A07F2A326D7

Russell, C., Kusner, M.J., Loftus, J., & Silva, R. (2017). When worlds collide: integrating different counterfactual assumptions in fairness. In Advances in Neural Information Processing Systems (pp. 6414-6423). Retrieved from https://papers.nips.cc/paper/7220-when-worlds-collide-integrating-different-counterfactual-assumptions-in-fairness.pdf

Shorrocks, A.F. (1980). The class of additively decomposable inequality measures. Econometrica: Journal of the Econometric Society, 613-625. Retrieved from http://www.vcharite.univ-mrs.fr/PP/lubrano/atelier/shorrocks1980.pdf

Speicher, T., Heidari, H., Grgic-Hlaca, N., Gummadi, K.P., Singla, A., Weller, A., & Zafar, M.B. (2018, July). A unified approach to quantifying algorithmic unfairness: Measuring individual &group unfairness via inequality indices. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 2239-2248). Retrieved from https://arxiv.org/pdf/1807.00787.pdf

Steinberg, D., Reid, A., & O’Callaghan, S. (2020). Fairness Measures for Regression via Probabilistic Classification. arXiv preprint retrieved from https://arxiv.org/pdf/2001.06089

Sylvester, J., & Raff, E. (2018). What about applied fairness?. arXiv preprint arXiv:1806.05250.

Verma, S. and Rubin, J., 2018, May. Fairness definitions explained. In 2018 ieee/acm international workshop on software fairness (fairware) (pp. 1-7). IEEE. doi: 10.1145/3194770.3194776

Xu, D., Yuan, S., & Wu, X. (2019, May). Achieving differential privacy and fairness in logistic regression. In Companion Proceedings of The 2019 World Wide Web Conference (pp. 594-599). Retrieved from https://dl.acm.org/doi/abs/10.1145/3308560.3317584

Zafar, M.B., Valera, I., Rogriguez, M.G., & Gummadi, K.P. (2017, April). Fairness constraints: Mechanisms for fair classification. In Artificial Intelligence and Statistics (pp. 962-970). PMLR. Available at http://proceedings.mlr.press/v54/zafar17a.html

Zafar, M.B., Valera, I., Gomez Rodriguez, M., & Gummadi, K.P. (2017, April). Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment. In Proceedings of the 26th international conference on world wide web (pp. 1171-1180). Retrieved from https://arxiv.org/pdf/1610.08452.pdf

Zemel, R., Wu, Y., Swersky, K., Pitassi, T., & Dwork, C. (2013, February). Learning fair representations. International Conference on Machine Learning (pp. 325-333). Retrieved from http://proceedings.mlr.press/v28/zemel13.pdf

Recorded References

Crawford, K. (2017, December). The Trouble with Bias [Conference presentation]. NeurIPS 2017, Long Beach, CA. https://youtu.be/fMym_BKWQzk

Stucchio, C. (2018, October). AI Ethics, Impossibility Theorems and Tradeoffs [Conference presentation]. Crunch Data Conference 2018, Budapest, Hungary. https://www.youtube.com/watch?v=Zn7oWIhFffs

Narayanan, A. (2018, March). FAT* 2018 Translation Tutorial: 21 Definitions of Fairness and Their Politics [Conference presentation]. The Conference on Fairness, Accountability, and Transparency (FAT*) 2018. https://www.youtube.com/watch?v=wqamrPkF5kk

Other Fairness Libraries of Note

Other Resources and Tutorials

Zhong, Z. (2018). “A Tutorial on Fairness in Machine Learning”. Towards Data Science. https://towardsdatascience.com/a-tutorial-on-fairness-in-machine-learning-3ff8ba1040cb

Cortez, V. (2019). “How to define fairness to detect and prevent discriminatory outcomes in Machine Learning”. Towards Data Science. https://towardsdatascience.com/how-to-define-fairness-to-detect-and-prevent-discriminatory-outcomes-in-machine-learning-ef23fd408ef2#:~:text=Demographic%20Parity%20states%20that%20the,%E2%80%9Cbeing%20shown%20the%20ad%E2%80%9D

Google People + AI Research (PAIR). PAIR Explorables: Measuring Fairness. https://pair.withgoogle.com/explorables/measuring-fairness/