If a user is presented an AI system that portends to explain how it works, how do we know
whether the explanation works and the user has achieved a pragmatic understanding of the …

Artificial Intelligence (AI) algorithms are increasingly providing decision making and
operational support across multiple domains. AI includes a wide (and growing) library of …

We introduce four principles for explainable artificial intelligence (AI) that comprise
fundamental properties for explainable AI systems. We propose that explainable AI systems …

Over the last few years there has been rapid research growth into eXplainable Artificial
Intelligence (XAI) and the closely aligned Interpretable Machine Learning (IML). Drivers for …

Recent advances in machine learning have led to growing interest in Explainable AI (xAI) to
enable humans to gain insight into the decision-making of machine learning models …

The widespread use of Large Language Model (LLM)-based conversational agents (CAs),
especially in high-stakes domains, raises many privacy concerns. Building ethical LLM …

Artificial intelligence (AI) and especially reinforcement learning (RL) have the potential to
enable agents to learn and perform tasks autonomously with superhuman performance …

Machine learning models need to provide contrastive explanations, since people often seek
to understand why a puzzling prediction occurred instead of some expected outcome …

Counterfactual explanations, which deal with “why not?” scenarios, can provide insightful
explanations to an AI agent's behavior [Miller [38]]. In this work, we focus on generating …

Online symptom checkers (OSC) are widely used intelligent systems in health contexts such
as primary care, remote healthcare, and epidemic control. OSCs use algorithms such as …