# FERI: A Multitask-based Fairness Achieving Algorithm with Applications to Fair Organ Transplantation

arXiv: https://arxiv.org/abs/2310.13820 · HTML: https://mlsanity.com/paper/2310.13820/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 44 references that are in the map.
- First posted on arXiv 20 Oct 2023.

## Builds on (most cited first, 22 of 22)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks](https://mlsanity.com/paper/1711.02257/) (2017)
- [A Reductions Approach to Fair Classification](https://mlsanity.com/paper/1803.02453/) (2018)
- [TabTransformer: Tabular Data Modeling Using Contextual Embeddings](https://mlsanity.com/paper/2012.06678/) (2020)
- [On Fairness and Calibration](https://mlsanity.com/paper/1709.02012/) (2017)
- [Fairness in Deep Learning: A Computational Perspective](https://mlsanity.com/paper/1908.08843/) (2019)
- [Learning Optimal Fair Policies](https://mlsanity.com/paper/1809.02244/) (2018)
- [An Empirical Characterization of Fair Machine Learning For Clinical Risk Prediction](https://mlsanity.com/paper/2007.10306/) (2020)
- [FAE: A Fairness-Aware Ensemble Framework](https://mlsanity.com/paper/2002.00695/) (2020)
- [Boundary Loss for Remote Sensing Imagery Semantic Segmentation](https://mlsanity.com/paper/1905.07852/) (2019)
- [FORML: Learning to Reweight Data for Fairness](https://mlsanity.com/paper/2202.01719/) (2022)
- [Maximum Weighted Loss Discrepancy](https://mlsanity.com/paper/1906.03518/) (2019)
- [Towards Fair Deep Clustering With Multi-State Protected Variables](https://mlsanity.com/paper/1901.10053/) (2019)
- [Towards Model-Agnostic Post-Hoc Adjustment for Balancing Ranking Fairness and Algorithm Utility](https://mlsanity.com/paper/2006.08267/) (2020)
- [Implicit Rate-Constrained Optimization of Non-decomposable Objectives](https://mlsanity.com/paper/2107.10960/) (2021)
- [Fairly Predicting Graft Failure in Liver Transplant for Organ Assigning](https://mlsanity.com/paper/2302.09400/) (2023)
- [Debiasing Neural Retrieval via In-batch Balancing Regularization](https://mlsanity.com/paper/2205.09240/) (2022)
- [A Transformer-Based Deep Learning Approach for Fairly Predicting Post-Liver Transplant Risk Factors](https://mlsanity.com/paper/2304.02780/) (2023)
- [Multi-Task Learning for Post-transplant Cause of Death Analysis: A Case Study on Liver Transplant](https://mlsanity.com/paper/2304.00012/) (2023)
- [Predicting multiple sclerosis disease severity with multimodal deep neural networks](https://mlsanity.com/paper/2304.04062/) (2023)

## Built on by (most cited first, 2 of 2)

- [U-Fair: Uncertainty-based Multimodal Multitask Learning for Fairer Depression Detection](https://mlsanity.com/paper/2501.09687/) (2025)
- [Is Fairness Truly Fair? Towards Reliable Lipschitz Fairness in Multi-Task Learning via Fixed-texorpdfstringδdelta Alignment](https://mlsanity.com/paper/2606.10632/) (2026)

## 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.