FERI: A Multitask-based Fairness Achieving Algorithm with Applications to Fair Organ Transplantation
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Abstract (arXiv)
Liver transplantation often faces fairness challenges across subgroups defined by sensitive attributes such as age group, gender, and race/ethnicity. Machine learning models for outcome prediction can introduce additional biases. Therefore, we introduce Fairness through the Equitable Rate of Improvement in Multitask Learning (FERI) algorithm for fair predictions of graft failure risk in liver transplant patients. FERI constrains subgroup loss by balancing learning rates and preventing subgroup dominance in the training process. Our results show that FERI maintained high predictive accuracy with AUROC and AUPRC comparable to baseline models. More importantly, FERI demonstrated an ability to improve fairness without sacrificing accuracy. Specifically, for the gender, FERI reduced the demographic parity disparity by 71.74%, and for the age group, it decreased the equalized odds disparity by 40.46%. Therefore, the FERI algorithm advanced fairness-aware predictive modeling in healthcare and provides an invaluable tool for equitable healthcare systems.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 22 of its 44 references that are in the map. First posted on arXiv 20 Oct 2023.
Builds on
22 of 44 references.
References · 22
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- 2022FORML: Learning to Reweight Data for Fairnesscited by 12
- 2019Maximum Weighted Loss Discrepancycited by 11
- 2019Towards Fair Deep Clustering With Multi-State Protected Variablescited by 9
- 2020Towards Model-Agnostic Post-Hoc Adjustment for Balancing Ranking Fairness and Algorithm Utilitycited by 9
- 2021Implicit Rate-Constrained Optimization of Non-decomposable Objectivescited by 9
- 2023Fairly Predicting Graft Failure in Liver Transplant for Organ Assigningcited by 7
- 2022Debiasing Neural Retrieval via In-batch Balancing Regularizationcited by 5
- 2023A Transformer-Based Deep Learning Approach for Fairly Predicting Post-Liver Transplant Risk Factorscited by 4
- 2023Multi-Task Learning for Post-transplant Cause of Death Analysis: A Case Study on Liver Transplantcited by 2
- 2023Predicting multiple sclerosis disease severity with multimodal deep neural networkscited by 1
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.