Fairness-Aware Multiobjective Evolutionary Learning

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 optimization objectives of MOEL throughout model training. However, the determination of a representative measure set relies on the dataset, prior knowledge, and requires substantial computational costs. What is more, those representative measures may differ across different model training processes. Instead of using a static predefined set determined before model training, this article proposes to dynamically and adaptively determine a representative measure set online during the model training. The dynamically determined representative set is then used as optimizing 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 the 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 optimization objectives. The results indicate the importance of setting optimization objectives dynamically during training.