With the widespread use of artificial intelligence (AI) systems and applications in our
everyday lives, accounting for fairness has gained significant importance in designing and …

Addressing the problem of fairness is crucial to safely use machine learning algorithms to
support decisions with a critical impact on people's lives such as job hiring, child …

Graph neural networks (GNNs) have shown great power in modeling graph structured data.
However, similar to other machine learning models, GNNs may make predictions biased on …

Fairness-aware learning is increasingly important in data mining. Discrimination prevention
aims to prevent discrimination in the training data before it is used to conduct predictive …

Abstract Machine learning based systems are reaching society at large and in many aspects
of everyday life. This phenomenon has been accompanied by concerns about the ethical …

The increasing use of data-driven decision support systems in industry and governments is
accompanied by the discovery of a plethora of bias and unfairness issues in the outputs of …

A recent trend of fair machine learning is to define fairness as causality-based notions which
concern the causal connection between protected attributes and decisions. However, one …

Algorithmic fairness has attracted significant attention in recent years, with many quantitative
measures suggested for characterizing the fairness of different machine learning algorithms …

Fairness-aware learning studies the problem of building machine learning models that are
subject to fairness requirements. Counterfactual fairness is a notion of fairness derived from …

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 …