Overview

The rapid development of generative AI systems, particularly large language models (LLMs), has generated both widespread excitement and growing concern. While these systems exhibit remarkable fluency and reasoning capabilities, they remain prone to a critical limitation known as hallucination, the confident production of inaccurate, unverifiable, or fabricated information. This phenomenon underscores a fundamental challenge in the reliability and trustworthiness of AI technologies. As a result, there is a pressing need for research that examines the theoretical foundations of AI hallucination, as well as approaches for its detection, mitigation, and broader socio-technical implications. Hallucination-Aware AI for Truthful and Aligned Systems examines the corrective measures needed to address the widespread impact of AI hallucinations, drawing on the insights and practices of leading experts to confront one of the most pressing challenges in contemporary AI. Through a multidisciplinary approach, the book advances the conversation around generative AI by exploring methods to improve reliability, alignment, and trustworthiness across diverse application domains. Covering topics such as augmenting clinical expertise, evolution of hallucination, and evaluating and detecting hallucinations, this book is a critical academic resource for graduate and doctoral students, data scientists, machine learning engineers, innovation leads, tech consultants, policymakers, and more.

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