Inspired by the increasing use of artificial intelligence (AI) to augment humans, researchers
have studied human–AI systems involving different tasks, systems and populations. Despite …

The diversity of topics in education makes it difficult for artificial intelligence (AI) to address
them all in depth. Therefore, guiding to focus efforts on specific issues is essential. The …

Recent work has proposed artificial intelligence (AI) models that can learn to decide whether
to make a prediction for an instance of a task or to delegate it to a human by considering …

In this paper we explore the effect of explanations on reducing errors in the human decision
making process caused by placing excessive reliance on automated decision support …

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 …

In this work, we study the effects of feature-based explanations on distributive fairness of AI-
assisted decisions, specifically focusing on the task of predicting occupations from short …

Artificial intelligence (AI)-driven generative chatbots can produce large quantities of text
instantly across a range of domains, using authoritative tones that create the perception of …

Explainable artificial intelligence (XAI), known to produce explanations so that predictions
from AI models can be understood, is commonly used to mitigate possible AI mistrust. The …

Abstract Explainable Artificial Intelligence (XAI) enables Artificial Intelligence (AI) to explain
its decisions. This holds the promise of making AI more understandable to users, improving …