The ability to understand and explain the outcomes of data analysis methods, with regard to
aiding decision-making, has become a critical requirement for many applications. For …

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

Machine learning models are becoming pervasive in high-stakes applications. Despite their
clear benefits in terms of performance, the models could show discrimination against …

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

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

Increasingly larger number of software systems today are including data science
components for descriptive, predictive, and prescriptive analytics. The collection of data …

A major factor in the recent success of large language models is the use of enormous and
ever-growing text datasets for unsupervised pre-training. However, naively training a model …

Trustworthy artificial intelligence (TAI) has proven invaluable in curbing potential negative
repercussions tied to AI applications. Within the TAI spectrum, federated learning (FL) …

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 …

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 …