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 …

Subject matter experts (SMEs) often rely on attribute importance rankings to verify machine
learning models, acquire insights into their outcomes, and gain a deeper understanding of …

Fairness concerns have recently been gaining increasing attention in machine learning (ML)
research and applications. ML models typically require massive data, which can be costly …

Abstract Machine learning methods based on AdaBoost have been widely applied to
various classification problems across many mission-critical applications including …

Machine learning is widely used to make decisions with societal impact such as bank loan
approving, criminal sentencing, and resume filtering. How to ensure its fairness while …

Abstract Pre-trained Generative Adversarial Networks (GANs) can provide rich information
and make various downstream tasks beneficial. However, the training process of GANs …

Fairness-aware mining of data streams is a challenging concern in the contemporary
domain of machine learning. Many stream learning algorithms are used to replace humans …

While most generative models show achievements in image data generation, few are
developed for tabular data generation. Recently, due to success of large language models …

An intrusion detection system (IDS) is valuable for detecting anomalies and unauthorized
access to a system or network. Due to the black‐box nature of these IDS models, network …