Advances in computer science and data-analytic methods are driving a new era in mental
health research and application. Artificial intelligence (AI) technologies hold the potential to …

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 …

Ensuring alignment, which refers to making models behave in accordance with human
intentions [1, 2], has become a critical task before deploying large language models (LLMs) …

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 …

Despite a surge of recent advances in promoting machine Learning (ML) fairness, the
existing mainstream approaches mostly require training or finetuning the entire weights of …

Recently, the user-side fairness issue in Collaborative Filtering (CF) algorithms has gained
considerable attention, arguing that results should not discriminate an individual or a sub …

Fairness has become an important topic in machine learning. Generally, most literature on
fairness assumes that the sensitive information, such as gender or race, is present in the …

Ensuring equitable impact of machine learning models across different societal groups is of
utmost importance for real-world machine learning applications. Prior research in fairness …

A variety of fairness constraints have been proposed in the literature to mitigate group-level
statistical bias. Their impacts have been largely evaluated for different groups of populations …

Graph diffusion problems such as the propagation of rumors, computer viruses, or smart grid
failures are ubiquitous and societal. Hence it is usually crucial to identify diffusion sources …