Recent advancements in general-purpose AI have highlighted the importance of guiding AI
systems towards the intended goals, ethical principles, and values of individuals and …

In this critical survey, we analyze typical claims on the relationship between explainable AI
(XAI) and fairness to disentangle the multidimensional relationship between these two …

Widely deployed large language models (LLMs) can produce convincing yet incorrect
outputs, potentially misleading users who may rely on them as if they were correct. To …

Group decision making plays a crucial role in our complex and interconnected world. The
rise of AI technologies has the potential to provide data-driven insights to facilitate group …

The current literature on AI‐advised decision making—involving explainable AI systems
advising human decision makers—presents a series of inconclusive and confounding …

Large language models (LLMs) are prone to hallucinations, ie, nonsensical, unfaithful, and
undesirable text. Users tend to overrely on LLMs and corresponding hallucinations which …

In AI-assisted decision-making, it is crucial but challenging for humans to achieve
appropriate reliance on AI. This paper approaches this problem from a human-centered …

The adoption of recommender systems (RSs) in various domains has become increasingly
popular, but concerns have been raised about their lack of transparency and interpretability …

Explainability techniques are rapidly being developed to improve human-AI decision-
making across various cooperative work settings. Consequently, previous research has …

AI assistance in decision-making has become popular, yet people's inappropriate reliance
on AI often leads to unsatisfactory human-AI collaboration performance. In this paper …