Algorithmic fairness plays an important role in machine learning and imposing fairness
constraints during learning is a common approach. However, many datasets are imbalanced …

Algorithmic decision making is employed in an increasing number of real-world applications
to aid human decision making. While it has shown considerable promise in terms of …

In this work, we consider the problem of intersectional group fairness in the classification
setting, where the objective is to learn discrimination-free models in the presence of several …

As an important problem in modern data analytics, classification has witnessed varieties of
applications from different domains. Different from conventional classification approaches …

We propose a novel algorithm for learning fair representations that can simultaneously
mitigate two notions of disparity among different demographic subgroups in the classification …

In this paper, we cast fair machine learning as invariant machine learning. We first formulate
a version of individual fairness that enforces invariance on certain sensitive sets. We then …

We present GetFair, a novel framework for tuning fairness of classification models. The fair
classification problem deals with training models for a given classification task where data …

Devising a fair classifier that does not discriminate against different groups is an important
problem in machine learning. Although researchers have proposed various ways of defining …

There is growing awareness that AI and machine learning systems can in some cases learn
to behave in unfair and discriminatory ways with harmful consequences. However, despite …

Motivated by concerns surrounding the fairness effects of sharing and transferring fair
machine learning tools, we propose two algorithms: Fairness Warnings and Fair-MAML. The …