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 …

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 …

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 …

Federated learning is a method of training models on private data distributed over multiple
devices. To keep device data private, the global model is trained by only communicating …

Recommendation, information retrieval, and other information access systems pose unique
challenges for investigating and applying the fairness and non-discrimination concepts that …

As one of the most pervasive applications of machine learning, recommender systems are
playing an important role on assisting human decision making. The satisfaction of users and …

Conventional graph neural networks (GNNs) are often confronted with fairness issues that
may stem from their input, including node attributes and neighbors surrounding a node …

Federated learning has received increasing attention for its ability to collaborative learning
without leaking privacy. Promising advances have been achieved under the assumption that …

As one of the most pervasive applications of machine learning, recommender systems are
playing an important role on assisting human decision-making. The satisfaction of users and …

Fair representation learning provides an effective way of enforcing fairness constraints
without compromising utility for downstream users. A desirable family of such fairness …