AI systems are adopted in numerous domains due to their increasingly strong predictive
performance. However, in high-stakes domains such as criminal justice and healthcare, full …

Explainable AI (XAI) is widely viewed as a sine qua non for ever-expanding AI research. A
better understanding of the needs of XAI users, as well as human-centered evaluations of …

Do large language models (LLMs) know the law? LLMs are increasingly being used to
augment legal practice, education, and research, yet their revolutionary potential is …

Prior work has identified a resilient phenomenon that threatens the performance of human-
AI decision-making teams: overreliance, when people agree with an AI, even when it is …

Recent advances in Large Language Models (LLM) have made automatic code generation
possible for real-world programming tasks in general-purpose programming languages …

The proposed EU regulation for Artificial Intelligence (AI), the AI Act, has sparked some
debate about the role of explainable AI (XAI) in high-risk AI systems. Some argue that black …

The rise of powerful large language models (LLMs) brings about tremendous opportunities
for innovation but also looming risks for individuals and society at large. We have reached a …

In this paper, we argue for a paradigm shift from the current model of explainable artificial
intelligence (XAI), which may be counter-productive to better human decision making. In …

AI explanations are often mentioned as a way to improve human-AI decision-making, but
empirical studies have not found consistent evidence of explanations' effectiveness and, on …

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 …