FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Data

• URL: http://arxiv.org/abs/2206.02792v1
• Date: Mon, 6 Jun 2022 04:39:25 GMT
• Title: FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Data
• Authors: Zhun Deng, Jiayao Zhang, Linjun Zhang, Ting Ye, Yates Coley, Weijie J. Su, James Zou
• Abstract summary: We propose a theoretically-principled, yet Flexible approach that is Imbalance-Fairness-Aware (FIFA) FIFA encourages both classification and fairness generalization and can be flexibly combined with many existing fair learning methods with logits-based losses. We demonstrate the power of FIFA by combining it with a popular fair classification algorithm, and the resulting algorithm achieves significantly better fairness generalization on several real-world datasets.
• Score: 34.70704786008873
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Algorithmic fairness plays an important role in machine learning and imposing fairness constraints during learning is a common approach. However, many datasets are imbalanced in certain label classes (e.g. "healthy") and sensitive subgroups (e.g. "older patients"). Empirically, this imbalance leads to a lack of generalizability not only of classification, but also of fairness properties, especially in over-parameterized models. For example, fairness-aware training may ensure equalized odds (EO) on the training data, but EO is far from being satisfied on new users. In this paper, we propose a theoretically-principled, yet Flexible approach that is Imbalance-Fairness-Aware (FIFA). Specifically, FIFA encourages both classification and fairness generalization and can be flexibly combined with many existing fair learning methods with logits-based losses. While our main focus is on EO, FIFA can be directly applied to achieve equalized opportunity (EqOpt); and under certain conditions, it can also be applied to other fairness notions. We demonstrate the power of FIFA by combining it with a popular fair classification algorithm, and the resulting algorithm achieves significantly better fairness generalization on several real-world datasets.

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