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 …

This article provides a comprehensive overview of the fairness issues in artificial intelligence
(AI) systems, delving into its background, definition, and development process. The article …

Recommender systems are an essential tool to relieve the information overload challenge
and play an important role in people's daily lives. Since recommendations involve …

While recent years have witnessed a rapid growth of research papers on recommender
system (RS), most of the papers focus on inventing machine learning models to better fit …

Graph Neural Networks (GNNs) have shown superior performance in analyzing attributed
networks in various web-based applications such as social recommendation and web …

Data-driven predictive models are increasingly used in education to support students,
instructors, and administrators, which has raised concerns about the fairness of their …

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 machine learning aims to mitigate the biases of model predictions against certain
subpopulations regarding sensitive attributes such as race and gender. Among the many …

Recent discoveries have revealed that deep neural networks might behave in a biased
manner in many real-world scenarios. For instance, deep networks trained on a large-scale …

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 …