Fairness-aware Multiobjective Evolutionary Learning

TL;DR

This paper introduces a dynamic approach to selecting fairness measures during multiobjective evolutionary learning, improving fairness and accuracy across diverse datasets by adapting objectives in real-time.

Contribution

It proposes a novel online method for dynamically selecting fairness measures as optimization objectives during training, enhancing fairness and efficiency.

Findings

Outperforms state-of-the-art fairness mitigation methods.

Achieves high accuracy and fairness across 12 benchmark datasets.

Demonstrates the benefits of dynamic objective selection in MOEL.

Abstract

Multiobjective evolutionary learning (MOEL) has demonstrated its advantages of training fairer machine learning models considering a predefined set of conflicting objectives, including accuracy and different fairness measures. Recent works propose to construct a representative subset of fairness measures as optimisation objectives of MOEL throughout model training. However, the determination of a representative measure set relies on dataset, prior knowledge and requires substantial computational costs. What's more, those representative measures may differ across different model training processes. Instead of using a static predefined set determined before model training, this paper proposes to dynamically and adaptively determine a representative measure set online during model training. The dynamically determined representative set is then used as optimising objectives of the MOEL framework and can vary with time. Extensive experimental results on 12 well-known benchmark datasets demonstrate that our proposed framework achieves outstanding performance compared to state-of-the-art approaches for mitigating unfairness in terms of accuracy as well as 25 fairness measures although only a few of them were dynamically selected and used as optimisation objectives. The results indicate the importance of setting optimisation objectives dynamically during training.