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

Increasingly, software is making autonomous decisions in case of criminal sentencing,
approving credit cards, hiring employees, and so on. Some of these decisions show bias …

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

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

Neural networks have had discernible achievements in a wide range of applications. The
wide-spread adoption also raises the concern of their dependability and reliability. Similar to …

Machine Learning (ML) software can lead to unfair and unethical decisions, making software
fairness bugs an increasingly significant concern for software engineers. However …

Recently, with the widespread use of deep neural networks (DNNs) in high-stakes decision-
making systems (such as fraud detection and prison sentencing), concerns have arisen …

With the widespread application of machine learning (ML) software, especially in high-risk
tasks, the concern about their unfairness has been raised towards both developers and …

The increasingly wide uptake of Machine Learning (ML) has raised the significance of the
problem of tackling bias (ie, unfairness), making it a primary software engineering concern …

Inspired by the great success of using code coverage as guidance in software testing, a lot
of neural network coverage criteria have been proposed to guide testing of neural network …