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 …

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 …

Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …

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 …

The rapid development of Artificial Intelligence (AI) technology has enabled the deployment
of various systems based on it. However, many current AI systems are found vulnerable to …

Objectives Leveraging artificial intelligence (AI) in conjunction with electronic health records
(EHRs) holds transformative potential to improve healthcare. However, addressing bias in …

Machine Learning (ML) software can lead to unfair and unethical decisions, making software
fairness bugs an increasingly significant concern for software engineers. However …

Collaborative filtering based recommendation learns users' preferences from all users'
historical behavior data, and has been popular to facilitate decision making. Recently, the …

Abstract Medical applications of Artificial Intelligence (AI) have consistently shown
remarkable performance in providing medical professionals and patients with support for …

With the increasing influence of machine learning algorithms in decision-making processes,
concerns about fairness have gained significant attention. This area now offers significant …