As decision‐making increasingly relies on machine learning (ML) and (big) data, the issue
of fairness in data‐driven artificial intelligence systems is receiving increasing attention from …

When Machine Learning technologies are used in contexts that affect citizens, companies as
well as researchers need to be confident that there will not be any unexpected social …

Traditional machine learning paradigms are based on the assumption that both training and
test data follow the same statistical pattern, which is mathematically referred to as …

Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …

Learning models that gracefully handle distribution shifts is central to research on domain
generalization, robust optimization, and fairness. A promising formulation is domain …

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 …

In the past few decades, artificial intelligence (AI) technology has experienced swift
developments, changing everyone's daily life and profoundly altering the course of human …

Recent progress in engineering highly promising biocatalysts has increasingly involved
machine learning methods. These methods leverage existing experimental and simulation …

The goal of sex and gender analysis is to promote rigorous, reproducible and responsible
science. Incorporating sex and gender analysis into experimental design has enabled …

A snapshot of the frontiers of fairness in machine learning Page 1 82 COMMUNICATIONS OF
THE ACM | MAY 2020 | VOL. 63 | NO. 5 review articles ILL US TRA TION B Y JUS TIN METZ …