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 …

This report synthesizes~ 60 research papers about overreliance on AI. The papers originate
from a variety of disciplines, including Human-Computer Interaction (HCI); Human Factors; …

Abstract Previous research in Explainable Artificial Intelligence (XAI) suggests that a main
aim of explainability approaches is to satisfy specific interests, goals, expectations, needs …

Understanding black box models has become paramount as systems based on opaque
Artificial Intelligence (AI) continue to flourish in diverse real-world applications. In response …

AI-assisted decision-making that impacts individuals raises critical questions about
transparency and fairness in artificial intelligence (AI). Much research has highlighted the …

Abstract Current developments in Artificial Intelligence (AI) led to a resurgence of
Explainable AI (XAI). New methods are being researched to obtain information from AI …

Machine learning models in safety-critical settings like healthcare are often “blackboxes”:
they contain a large number of parameters which are not transparent to users. Post-hoc …

Hybrid Intelligence is an emerging concept that emphasizes the complementary nature of
human intelligence and artificial intelligence (AI). One key requirement for collaboration …

Ethical considerations, including transparency, play an important role when using artificial
intelligence (AI) in education. Explainable AI has been coined as a solution to provide more …

Recent advances in Large Language Models (LLMs) show the potential to significantly
augment or even replace complex human writing activities. However, for complex tasks …