This paper surveys recent work in the intersection of differential privacy (DP) and fairness. It
reviews the conditions under which privacy and fairness may have aligned or contrasting …

Ensuring fairness in machine learning (ML) is crucial, particularly in applications that impact
diverse populations. The majority of existing works heavily rely on the availability of …

Machine learning (ML) models have been deployed for high-stakes applications. Due to
class imbalance in the sensitive attribute observed in the datasets, ML models are unfair on …

Ethical bias in machine learning models has become a matter of concern in the software
engineering community. Most of the prior software engineering works concentrated on …

Abstract Machine learning based systems are reaching society at large and in many aspects
of everyday life. This phenomenon has been accompanied by concerns about the ethical …

Machine learning algorithms are increasingly used to make or support important decisions
about people's lives. This has led to interest in the problem of fair classification, which …

Pre-trained Large Language Models (LLMs) are an integral part of modern AI that have led
to breakthrough performances in complex AI tasks. Major AI companies with expensive …

As the decisions made or influenced by machine learning models increasingly impact our
lives, it is crucial to detect, understand, and mitigate unfairness. But even simply determining …

We review and reflect on fairness notions proposed in machine learning literature and make
an attempt to draw connections to arguments in moral and political philosophy, especially …

Accuracy and individual fairness are both crucial for trustworthy machine learning, but these
two aspects are often incompatible with each other so that enhancing one aspect may …