FIPSER: Improving Fairness Testing of DNN by Seed Prioritization

Abstract

As a rapidly evolving AI technology, deep neural networks are becoming increasingly integrated into human society, yet raising concerns about fairness issues. Previous studies have proposed a metric called causal fairness to measure the fairness of machine learning models and proposed some search algorithms to mine individual discrimination instance pairs (IDIPs). Fairness issues can be alleviated by retraining models with corrected IDIPs. However, the number of samples that are used as seeds for these methods is often limited due to the pursuit of efficiency. In addition, the quantity of IDIPs generated on different seeds varies, so it makes sense to select appropriate samples as seeds, which has not been sufficiently considered in past studies. In this paper, we study the imbalance in IDIP quantities for various datasets and sensitive attributes, highlighting the need for selecting and ranking seed samples. Then, we proposed FIPSER, a feature importance and perturbation potential-based seed prioritization method. Our experimental results show that, on average, when applied to the current state-of-the-art method of IDIP mining, FIPSER can improve its effectiveness by 45% and efficiency by 11%.