Many existing group fairness-aware training methods aim to achieve the group fairness by
either re-weighting underrepresented groups based on certain rules or using weakly …

Algorithmic recourse aims to recommend an informative feedback to overturn an
unfavorable machine learning decision. We introduce in this paper the Bayesian recourse, a …

Unwanted bias is a major concern in machine learning, raising in particular significant
ethical issues when machine learning models are deployed within high-stakes decision …

In this paper we present novel data-driven optimization models for Support Vector Machines
(SVM), with the aim of linearly separating two sets of points that have non-disjoint convex …

Covariate shift in the test data is a common practical phenomena that can significantly
downgrade both the accuracy and the fairness performance of the model. Ensuring fairness …

Existing group fairness-aware training methods fall into two categories: re-weighting
underrepresented groups according to certain rules, or using regularization terms such as …

This paper proposes a novel framework for certifying the fairness of predictive models
trained on biased data. It draws from query answering for incomplete and inconsistent …

We consider the problem of learning fair policies for multi-stage selection problems from
observational data. This problem arises in several high-stakes domains such as company …

A number of approaches has been proposed to investigate and mitigate unfairness in
machine learning algorithms. However, as the definition and understanding of fairness may …

A traditional stochastic program under a finite population typically seeks to optimize
efficiency by maximizing the expected profits or minimizing the expected costs, subject to a …