Unfair behaviors of Machine Learning (ML) software have garnered increasing attention and
concern among software engineers. To tackle this issue, extensive research has been …

The deep feedforward neural networks (DNNs) are increasingly deployed in socioeconomic
critical decision support software systems. DNNs are exceptionally good at finding min-imal …

Software bias is an increasingly important operational concern for software engineers. We
present a large-scale, comprehensive empirical study of 17 representative bias mitigation …

Machine Learning (ML) software has been widely adopted in modern society, with reported
fairness implications for minority groups based on race, sex, age, etc. Many recent works …

Fairness of machine learning (ML) software has become a major concern in the recent past.
Although recent research on testing and improving fairness have demonstrated impact on …

Deep Neural Networks (DNNs) are becoming an integral part of many real-world
applications, such as autonomous driving and financial management. While these models …

Deep neural networks (DNNs) have made significant progress, but often suffer from fairness
issues, as deep models typically show distinct accuracy differences among certain …

Existing research mostly improves the fairness of Machine Learning (ML) software regarding
a single protected attribute at a time, but this is unrealistic given that many users have …

Graph neural network (GNN) with a powerful representation capability has been widely
applied to various areas. Recent works have exposed that GNN is vulnerable to the …

In recent years, artificial intelligence technology has been widely used in many fields, such
as computer vision, natural language processing and autonomous driving. Machine learning …