The use of machine learning (ML) in healthcare raises numerous ethical concerns,
especially as models can amplify existing health inequities. Here, we outline ethical …

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 …

Algorithmic fairness is frequently motivated in terms of a trade-off in which overall
performance is decreased so as to improve performance on disadvantaged groups where …

Fairness is now an important issue in machine learning. There are arising concerns that
automated decision-making systems reflect real-world biases. Although a wide range of …

Data-driven algorithms are studied and deployed in diverse domains to support critical
decisions, directly impacting people's well-being. As a result, a growing community of …

We theoretically study the impact of differential privacy on fairness in classification. We prove
that, given a class of models, popular group fairness measures are pointwise Lipschitz …

Abstract Fairness issues in Deep Learning models have recently received increasing
attention due to their significant societal impact. Although methods for mitigating unfairness …

In contrast to offline working fashions, two research paradigms are devised for online
learning:(1) Online Meta-Learning (OML)[6, 20, 26] learns good priors over model …

Algorithms are now routinely used to make consequential decisions that affect human lives.
Examples include college admissions, medical interventions or law enforcement. While …

A growing community of researchers has been investigating the equity of algorithms,
advancing the understanding of risks and opportunities of automated decision-making for …