The application of Artificial Intelligence (AI) across a wide range of domains comes with both
high expectations of its benefits and dire predictions of misuse. While AI systems have …

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 …

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 …

As Large Language Models (LLMs) gain in popularity, it is important to understand how
novice programmers use them and the effect they have on learning to code. We present the …

In AI-assisted decision-making, it is critical for human decision-makers to know when to trust
AI and when to trust themselves. However, prior studies calibrated human trust only based …

This study delves into the dual nature of artificial intelligence (AI), illuminating its
transformative potential that has the power to revolutionize various aspects of our lives. We …

Artificial intelligence (AI) has the potential to improve human decision-making by providing
decision recommendations and problem-relevant information to assist human decision …

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

Details of the designs and mechanisms in support of human-AI collaboration must be
considered in the real-world fielding of AI technologies. A critical aspect of interaction design …