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This book explores the evolving role of artificial intelligence in electoral processes, focusing on its potential to improve data-driven decision-making amid the growing challenges of misinformation, manipulation, and voter suppression. It discusses how AI tools—from chatbots to comprehensive data systems—could address information gaps for voters, candidates, and election commissions, especially during a pivotal election year like 2024, while acknowledging the skepticism and fears that often surround the use of AI in such critical civic functions.Drawing on insights from three specialized workshops at major AI conferences, the book compiles research and expert discussions from fields such as security, journalism, law, and political science. It serves as a comprehensive resource for researchers, educators, practitioners, students, and government officials, offering self-contained chapters that cover both technical and ethical aspects of employing AI in elections. The work also emphasizes the importance of maintaining high professional and ethical standards in the intersection of technology and democracy. This book will serve as an important resource on election topics, AI techniques and trust methods for researchers, teachers, practitioners, students and government officials in their efforts to improve democratic electoral processes with technology. It assumes the reader is knowledgeable, at high school level or higher, about one or more topics in civics and computing concepts. Sufficient background are given by contributors to make the chapters self-contained and widely understandable.
- Nyhet
Assessing, Explaining, and Rating AI Systems for Trust
With Applications in Finance
Inbunden, Engelska, 2026
693 kr
Kommande
This book discusses how to assess, explain, and rate the trustworthiness of artificial intelligence (AI) models and systems, and the authors use a causality-based rating approach to measure trust in AI models and tools, especially when using AI to make financial decisions. AI systems are currently 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.