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Explainable Agency in Artificial Intelligence : Research and Practice

By: (Edited by) David W. Aha , (Edited by) Silvia Tulli

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Ksh 9,550.00

Format: Paperback / Softback

ISBN-10: 1032392584

ISBN-13: 9781032392585

Series: Chapman & Hall/CRC Artificial Intelligence and Robotics Series

Publisher: Taylor & Francis Ltd

Imprint: CRC Press

Country of Manufacture: GB

Country of Publication: GB

Publication Date: Jan 22nd, 2024

Print length: 150 Pages

Weight: 306 grams

Dimensions (height x width x thickness): 15.60 x 23.40 x 1.60 cms

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The book is a collection of cutting-edge research on the topic of explainable agency in artificial intelligence (XAI), including counterfactuals, fairness, human evaluations, and iterative and active communication among agents.

This book focuses on a subtopic of explainable AI (XAI) called explainable agency (EA), which involves producing records of decisions made during an agent’s reasoning, summarizing its behavior in human-accessible terms, and providing answers to questions about specific choices and the reasons for them. We distinguish explainable agency from interpretable machine learning (IML), another branch of XAI that focuses on providing insight (typically, for an ML expert) concerning a learned model and its decisions. In contrast, explainable agency typically involves a broader set of AI-enabled techniques, systems, and stakeholders (e.g., end users), where the explanations provided by EA agents are best evaluated in the context of human subject studies.

The chapters of this book explore the concept of endowing intelligent agents with explainable agency, which is crucial for agents to be trusted by humans in critical domains such as finance, self-driving vehicles, and military operations. This book presents the work of researchers from a variety of perspectives and describes challenges, recent research results, lessons learned from applications, and recommendations for future research directions in EA. The historical perspectives of explainable agency and the importance of interactivity in explainable systems are also discussed. Ultimately, this book aims to contribute to the successful partnership between humans and AI systems.

Features:

  • Contributes to the topic of explainable artificial intelligence (XAI)
  • Focuses on the XAI subtopic of explainable agency
  • Includes an introductory chapter, a survey, and five other original contributions

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