In recent years, machine learning techniques have been increasingly applied in sensitive
decision making processes, raising fairness concerns. Past research has shown that …

Much of the work in the field of group fairness addresses disparities between predefined
groups based on protected features such as gender, age, and race, which need to be …

Machine learning (ML) is increasingly being used to make decisions in our society. ML
models, however, can be unfair to certain demographic groups (eg, African Americans or …

Achieving the Bayes optimal binary classification rule subject to group fairness constraints is
known to be reducible, in some cases, to learning a group-wise thresholding rule over the …

Many existing fairness criteria for machine learning involve equalizing some metric across
protected groups such as race or gender. However, practitioners trying to audit or enforce …

We initiate the study of fair classifiers that are robust to perturbations in the training
distribution. Despite recent progress, the literature on fairness has largely ignored the …

When building classification systems with demographic fairness considerations there are
two objectives to satisfy: 1) maximizing utility for the specific task and 2) ensuring fairness wrt …

The concern about underlying discrimination hidden in machine learning (ML) models is
increasing, as ML systems have been widely applied in more and more real-world scenarios …

Algorithmic fairness has gained prominence due to societal and regulatory concerns about
biases in Machine Learning models. Common group fairness metrics like Equalized Odds …

Recent research has shown that seemingly fair machine learning models, when used to
inform decisions that have an impact on peoples' lives or well-being (eg, applications …