Fair machine learning is receiving an increasing attention in machine learning fields.
Researchers in fair learning have developed correlation or association-based measures …

We study fair classification in the presence of an omniscient adversary that, given an $\eta $,
is allowed to choose an arbitrary $\eta $-fraction of the training samples and arbitrarily …

Abstract Machine learning is used to make decisions for individuals in various fields, which
require us to achieve good prediction accuracy while ensuring fairness with respect to …

We study three classical machine learning algorithms in the context of algorithmic fairness:
adaptive boosting, support vector machines, and logistic regression. Our goal is to maintain …

Despite the success of large-scale empirical risk minimization (ERM) at achieving high
accuracy across a variety of machine learning tasks, fair ERM is hindered by the …

Fairness in machine learning is getting rising attention as it is directly related to real-world
applications and social problems. Recent methods have been explored to alleviate the …

We show how to take a regression function that is appropriately multicalibrated and
efficiently post-process it into an approximately error minimizing classifier satisfying a large …

Recently, fairness-aware learning have become increasingly crucial, but most of those
methods operate by assuming the availability of fully annotated demographic group labels …

Fairness researchers in machine learning (ML) have coalesced around several fairness
criteria which provide formal definitions of what it means for an ML model to be fair …

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 …