AI systems are being deployed at large scale for practical applications, and it is important to define, measure, and communicate metrics that can indicate the trustworthiness of AI before using them to perform critical activities.
Despite their growing prevalence, there is a gap in understanding about how to assess AI-based systems effectively to ensure they are responsible, unbiased, and accurate. This book provides background information on cutting-edge AI trustworthiness to make essential decisions, and readers will learn how to think methodically with respect to explainability, causality, and factors affecting trustworthiness such as bias indication.
Additional topics include compliance with regulatory and market demands and an examination of the concept of a "trust score" or "trust rating" for AI systems where these metrics are reviewed, augmented, and applied to multiple AI examples.
It advances the view that trustworthiness is not an abstract concept or a binary property, but a multidimensional notion that can and should be evaluated in a structured and comparative manner that combines qualitative and quantitative criteria. This perspective resonates strongly with the growing recognition that responsible AI requires concrete operational mechanisms besides high-level principles.
This volume will be of interest to students and researchers seeking concrete and principled methods for assessing AI trustworthiness, to practitioners who must make decisions about AI deployment, and to policymakers and non-technical stakeholders who require interpretable and actionable assessments of AI behavior.
About the authors:
Kausik Lakkaraju is a doctoral candidate at the AI Institute of the University of South Carolina, specializing in evaluating AI systems through causal analysis. He has worked extensively with AI models spanning unimodal (text, image, and numeric/ time-series) and multimodal diversity in a variety of forms including foundations models, rule-based systems, and chatbots and with applications in finance, health, and education.
Biplav Srivastava, Ph.D., is a Professor of Computer Science at the AI Institute and Department of Computer Science at the University of South Carolina. With over three decades of AI experience in industry and academia, he directs the 'AI for Society' group, which investigates how to enable people to make rational decisions despite the real-world complexities of poor data, changing goals, and limited resources by augmenting their cognitive limitations with technology. Dr. Srivastava has been conducting research in trustworthy AI for a decade, introduced the first course on the topic at USC, and has led USC’s participation in the National Institute of Standards and Technology’s Artificial Intelligence Consortium since its inception in 2024.
Part of the book series: Synthesis Lectures on Computer Science (SLCS). The series publishes short books on general computer science topics that will appeal to advanced students, researchers, and practitioners in a variety of areas within computer science.
Publisher: Springer Nature (Print ISBN 978-3-032-21038-8, Online ISBN 978-3-032-21039-5)