Multi-task learning with dynamic re-weighting to achieve fairness in healthcare predictive modeling

Williams, 2016, Achieving equity in an evolving healthcare system: opportunities and challenges, Am. J. Med. Sci., 351, 33, 10.1016/j.amjms.2015.10.012 Artiga, 2020 Williams, 2008, Moving upstream: how interventions that address the social determinants of health can improve health and reduce disparities, J. Public Health Manag. Pract., 14, S8, 10.1097/01.PHH.0000338382.36695.42 Parry, 2003, The care transitions intervention: a patient-centered approach to ensuring effective transfers between sites of geriatric care, Home Health Care Serv. Q., 22, 1, 10.1300/J027v22n03_01 Obermeyer, 2019, Dissecting racial bias in an algorithm used to manage the health of populations, Science, 366, 447, 10.1126/science.aax2342 Linardatos, 2020, Explainable AI: A review of machine learning interpretability methods, Entropy, 23, 18, 10.3390/e23010018 Lee, 2019 Veale, 2017, Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data, Big Data Soc., 4, 10.1177/2053951717743530 Pessach, 2022, A review on fairness in machine learning, ACM Comput. Surv., 55, 1, 10.1145/3494672 Meng, 2022, Interpretability and fairness evaluation of deep learning models on MIMIC-IV dataset, Sci. Rep., 12, 7166, 10.1038/s41598-022-11012-2 Xu, 2022, Algorithmic fairness in computational medicine, EBioMedicine, 84, 10.1016/j.ebiom.2022.104250 Kim, 2022, An information theoretic approach to reducing algorithmic bias for machine learning, Neurocomputing, 500, 26, 10.1016/j.neucom.2021.09.081 Lohia, 2019, Bias mitigation post-processing for individual and group fairness, 2847 Petersen, 2021, Post-processing for individual fairness, Adv. Neural Inf. Process. Syst., 34, 25944 Vandenhende, 2022, Multi-Task learning for dense prediction tasks: A survey, IEEE Trans. Pattern Anal. Mach. Intell., 44, 3614 Bertsimas, 2011, The price of fairness, Oper. Res., 59, 17, 10.1287/opre.1100.0865 Mehrabi, 2021, A survey on bias and fairness in machine learning, ACM Comput. Surv., 54, 1, 10.1145/3457607 2023 Jiang, 2020, Identifying and correcting label bias in machine learning, 702 Kilbertus, 2020, Fair decisions despite imperfect predictions, 277 Xu, 2018, Fairgan: Fairness-aware generative adversarial networks, 570 L. Oneto, M. Doninini, A. Elders, M. Pontil, Taking advantage of multitask learning for fair classification, in: Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 2019, pp. 227–237. Tan, 2020, Learning fair representations for kernel models, 155 Pleiss, 2017, On fairness and calibration, Adv. Neural Inf. Process. Syst., 30 A. Noriega-Campero, M.A. Bakker, B. Garcia-Bulle, A. Pentland, Active fairness in algorithmic decision making, in: Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 2019, pp. 77–83. Iosifidis, 2019, Fae: A fairness-aware ensemble framework, 1375 Du, 2021, Fairness in deep learning: A computational perspective, IEEE Intell. Syst., 36, 25, 10.1109/MIS.2020.3000681 Agarwal, 2018, A reductions approach to fair classification, vol. 80, 60 Chuang, 2021 Ding, 2023 Ding, 2023 Liu, 2019 Ross, 2017 Kim, 2018, Fairness through computationally-bounded awareness, Adv. Neural Inf. Process. Syst., 31 Coston, 2019, Fair transfer learning with missing protected attributes, 91 Li, 2023 Cotter, 2019, Optimization with non-differentiable constraints with applications to fairness, recall, churn, and other goals, J. Mach. Learn. Res., 20, 1 Goh, 2016, Satisfying real-world goals with dataset constraints, Adv. Neural Inf. Process. Syst., 29 Gupta, 2018, Diminishing returns shape constraints for interpretability and regularization, Adv. Neural Inf. Process. Syst., 31 Zhang, 2022, Rethinking hard-parameter sharing in multi-domain learning, 01 Zhang, 2021, DBNet: a novel deep learning framework for mechanical ventilation prediction using electronic health records, 1 Zhang, 2023