We review and reflect on fairness notions proposed in machine learning literature and make
an attempt to draw connections to arguments in moral and political philosophy, especially …

Reinforcement learning is an essential paradigm for solving sequential decision problems
under uncertainty. Despite many remarkable achievements in recent decades, applying …

Evaluating fairness can be challenging in practice because the sensitive attributes of data
are often inaccessible due to privacy constraints. The go-to approach that the industry …

Recent studies successfully learned static graph embeddings that are structurally fair by
preventing the effectiveness disparity of high-and low-degree vertex groups in downstream …

Large language models (LLMs) can easily generate biased and discriminative responses.
As LLMs tap into consequential decision-making (eg, hiring and healthcare), it is of crucial …

We reveal and address the frequently overlooked yet important issue of disguised
procedural unfairness, namely, the potentially inadvertent alterations on the behavior of …

In sequential decision-making problems involving sensitive attributes like race and gender,
reinforcement learning (RL) agents must carefully consider long-term fairness while …

The widespread integration of Machine Learning systems in daily life, particularly in high-
stakes domains, has raised concerns about the fairness implications. While prior works have …

Explainable recommender systems, which aim to provide accurate recommendations and
reliable explanations, have attracted significant research interest due to their ability to …

In recent years, the concepts of``human-centered AI''and``responsible data science''have
gained prominence across multiple sectors, including academia, industry, government, and …