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 …

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 …

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 …

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 …

Artificial Intelligence (AI) has emerged as a transformative force in various domains,
including tax compliance and financial regulation. This review explores the pivotal role of AI …

Warning: this paper contains content that maybe offensive or upsetting. Recent research in
Natural Language Processing (NLP) has advanced the development of various toxicity …

We consider the problem of whether a Neural Network (NN) model satisfies global individual
fairness. Individual Fairness (defined in (Dwork et al. 2012)) suggests that similar individuals …

This article considers the problem of maintaining the long-term fairness of item exposure in
interactive recommender systems under the dynamic setting that user preference and item …

Despite the remarkable performances in various applications, machine learning (ML)
models could potentially discriminate. They may result in biasness in decision-making …

Predictive models in biomedicine need to ensure equitable and reliable outcomes for the
populations they are applied to. Unfortunately, biases in medical predictions can lead to …