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 …

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 …

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 …

Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler
of its great success is the availability of abundant and high-quality data for building machine …

Prediction-powered inference is a framework for performing valid statistical inference when
an experimental dataset is supplemented with predictions from a machine-learning system …

Training ML models which are fair across different demographic groups is of critical
importance due to the increased integration of ML in crucial decision-making scenarios such …

Different distribution shifts require different algorithmic and operational interventions.
Methodological research must be grounded by the specific shifts they address. Although …

As the scope of machine learning broadens, we observe a recurring theme of algorithmic
monoculture: the same systems, or systems that share components (eg datasets, models) …

An introduction to the intellectual foundations and practical utility of the recent work on
fairness and machine learning. Fairness and Machine Learning introduces advanced …

AI explanations are often mentioned as a way to improve human-AI decision-making, but
empirical studies have not found consistent evidence of explanations' effectiveness and, on …