This article provides a comprehensive survey of bias mitigation methods for achieving
fairness in Machine Learning (ML) models. We collect a total of 341 publications concerning …

Abstract Machine learning models always make a prediction, even when it is likely to be
inaccurate. This behavior should be avoided in many decision support applications, where …

We propose an analysis in fair learning that preserves the utility of the data while reducing
prediction disparities under the criteria of group sufficiency. We focus on the scenario where …

Selective classification (also known as classification with reject option) conservatively
extends a classifier with a selection function to determine whether or not a prediction should …

We present a novel Bayesian-based optimization framework that addresses the challenge of
generalization in overparameterized models when dealing with imbalanced subgroups and …

We consider the problem of identifying universal low-dimensional features from high-
dimensional data for inference tasks in settings involving learning. For such problems, we …

Selective regression allows abstention from prediction if the confidence to make an accurate
prediction is not sufficient. In general, by allowing a reject option, one expects the …

Learning with rejection is an important framework that can refrain from making predictions to
avoid critical mispredictions by balancing between prediction and rejection. Previous studies …

Increasing concerns about disparate effects of AI have motivated a great deal of work on fair
machine learning. Existing works mainly focus on independence-and separation-based …

We tackle the problem of Selective Classification where the objective is to achieve the best
performance on a predetermined ratio (coverage) of the dataset. Recent state-of-the-art …