In classification models fairness can be ensured by solving a constrained optimization
problem. We focus on fairness constraints like Disparate Impact, Demographic Parity, and …

In classification models, fairness can be ensured by solving a constrained optimization
problem. We focus on fairness constraints like Disparate Impact, Demographic Parity, and …

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 …

Group-fairness in classification aims for equality of a predictive utility across different
sensitive sub-populations, eg, race or gender. Equality or near-equality constraints in group …

As machine learning becomes prevalent in a widening array of sensitive applications such
as job hiring and criminal justice, one critical aspect that machine learning classifiers should …

As machine learning has been deployed ubiquitously across applications in modern data
science, algorithmic fairness has become a great concern. Among them, imposing fairness …

In past work on fairness in machine learning, the focus has been on forcing the prediction of
classifiers to have similar statistical properties for people of different demographics. To …

In machine learning fairness, training models which minimize disparity across different
sensitive groups often leads to diminished accuracy, a phenomenon known as the fairness …

We tackle the problem of group fairness in classification, where the objective is to learn
models that do not unjustly discriminate against subgroups of the population. Most existing …

Extensive efforts have been made to understand and improve the fairness of machine
learning models based on observational metrics, especially in high-stakes domains such as …