As machine learning becomes prevalent in our daily lives involving a widening array of
applications such as medicine, finance, job hiring and criminal justice, one morally & legally …

Training and evaluation of fair classifiers is a challenging problem. This is partly due to the
fact that most fairness metrics of interest depend on both the sensitive attribute information …

In recent years, machine learning has become more common in everyday applications.
Consequently, numerous studies have explored issues of unfairness against specific groups …

Fairness-aware machine learning has garnered significant attention in recent years because
of extensive use of machine learning in sensitive applications like judiciary systems. Various …

Group fairness definitions such as Demographic Parity and Equal Opportunity make
assumptions about the underlying decision-problem that restrict them to classification …

Machine learning algorithms may have disparate impacts on protected groups. To address
this, we develop methods for Bayes-optimal fair classification, aiming to minimize …

We propose a distributionally robust logistic regression model with an unfairness penalty
that prevents discrimination with respect to sensitive attributes such as gender or ethnicity …

While artificial intelligence (AI) and machine learning (ML) have been increasingly used for
decision-making, issues related to discrimination in AI/ML have become prominent. While …

Group fairness definitions such as Demographic Parity and Equal Opportunity make
assumptions about the underlying decision-problem that restrict them to classification …

Machine learning systems have been shown to propagate the societal errors of the past. In
light of this, a wealth of research focuses on designing solutions that are" fair." Even with this …