IFFair: Influence Function-driven Sample Reweighting for Fair Classification

TL;DR

IFFair is a pre-processing technique that uses influence functions to reweight training samples, effectively reducing bias and improving fairness in classification tasks without altering model architecture.

Contribution

Introduces IFFair, a novel influence function-based sample reweighting method that enhances fairness in classification without modifying the model or features.

Findings

Reduces bias across multiple fairness metrics

Achieves better utility-fairness trade-offs

Effective on real-world datasets

Abstract

Because machine learning has significantly improved efficiency and convenience in the society, it's increasingly used to assist or replace human decision-making. However, the data-based pattern makes related algorithms learn and even exacerbate potential bias in samples, resulting in discriminatory decisions against certain unprivileged groups, depriving them of the rights to equal treatment, thus damaging the social well-being and hindering the development of related applications. Therefore, we propose a pre-processing method IFFair based on the influence function. Compared with other fairness optimization approaches, IFFair only uses the influence disparity of training samples on different groups as a guidance to dynamically adjust the sample weights during training without modifying the network structure, data features and decision boundaries. To evaluate the validity of IFFair, we conduct experiments on multiple real-world datasets and metrics. The experimental results show that our approach mitigates bias of multiple accepted metrics in the classification setting, including demographic parity, equalized odds, equality of opportunity and error rate parity without conflicts. It also demonstrates that IFFair achieves better trade-off between multiple utility and fairness metrics compared with previous pre-processing methods.