AI Assurance

AI Assurance

Towards Trustworthy, Explainable, Safe, and Ethical AI

1st Edition - October 12, 2022

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  • Editors: Feras Batarseh, Laura Freeman
  • Paperback ISBN: 9780323919197
  • eBook ISBN: 9780323918824

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Description

AI Assurance: Towards Trustworthy, Explainable, Safe, and Ethical AI provides readers with solutions and a foundational understanding of the methods that can be applied to test AI systems and provide assurance. Anyone developing software systems with intelligence, building learning algorithms, or deploying AI to a domain-specific problem (such as allocating cyber breaches, analyzing causation at a smart farm, reducing readmissions at a hospital, ensuring soldiers’ safety in the battlefield, or predicting exports of one country to another) will benefit from the methods presented in this book. As AI assurance is now a major piece in AI and engineering research, this book will serve as a guide for researchers, scientists and students in their studies and experimentation. Moreover, as AI is being increasingly discussed and utilized at government and policymaking venues, the assurance of AI systems—as presented in this book—is at the nexus of such debates.

Key Features

  • Provides readers with an in-depth understanding of how to develop and apply Artificial Intelligence in a valid, explainable, fair and ethical manner
  • Includes various AI methods, including Deep Learning, Machine Learning, Reinforcement Learning, Computer Vision, Agent-Based Systems, Natural Language Processing, Text Mining, Predictive Analytics, Prescriptive Analytics, Knowledge-Based Systems, and Evolutionary Algorithms
  • Presents techniques for efficient and secure development of intelligent systems in a variety of domains, such as healthcare, cybersecurity, government, energy, education, and more
  • Covers complete example datasets that are associated with the methods and algorithms developed in the book

Readership

Scientists, researchers, and MSc. PhD. students from the fields of Computer Science and Engineering. The audience includes researchers, practitioners, and students in the fields of computer architecture and operating systems, as well as management information systems

Table of Contents

  • 1. An introduction to AI assurance
    2. Setting the goals for ethical, unbiased and fair AI
    3. An overview of explainable and interpretable AI
    4. Bias, Fairness, and assurance in AI: Overview and Synthesis
    5. An evaluation of the potential global impacts of AI assurance
    6. The role of inference in AI: start S.M.A.L.L. with mindful models
    7. Outlier detection using AI: a survey
    8. AI assurance using casual inference: application to public policy
    9. Data collection, wrangling and preprocessing for AI assurance
    10. Coordination-aware assurance for end-to-end machine learning systems: the R3E approach
    11. Assuring AI methods for economic policymaking
    12. Panopticon implications of ethical AI: equity, disparity, and inequality in healthcare
    13. Recent advances in uncertainty quantification methods for engineering problems
    14. Socially responsible AI assurance in precision agriculture for farmers and policymakers
    15. The application of AI assurance in precision farming and agricultural economics
    16. Bringing dark data to light with AI for evidence-based policy making

Product details

  • No. of pages: 600
  • Language: English
  • Copyright: © Academic Press 2022
  • Published: October 12, 2022
  • Imprint: Academic Press
  • Paperback ISBN: 9780323919197
  • eBook ISBN: 9780323918824

About the Editors

Feras Batarseh

Feras A. Batarseh is an Associate Professor with the Department of Biological Systems Engineering at Virginia Tech (VT) and the Director of A3 (AI Assurance and Applications) Lab. His research spans the areas of AI Assurance, Cyberbiosecurity, AI for Agriculture and Water, and Data-Driven Public Policy. His work has been published at various prestigious journals and international conferences. Additionally, Dr. Batarseh published multiple chapters and books, his two recent books are: "Federal Data Science", and "Data Democracy", both by Elsevier’s Academic Press. Dr. Batarseh is a senior member of the Institute of Electrical and Electronics Engineers (IEEE), the Agricultural and Applied Economical Association (AAEA), and the Association for the Advancement of Artificial Intelligence (AAAI). He has taught AI and Data Science courses at multiple universities including George Mason University (GMU), University of Maryland - Baltimore County (UMBC), Georgetown University, and George Washington University (GWU). Dr. Batarseh obtained his Ph.D. and M.Sc. in Computer Engineering from the University of Central Florida (UCF) (2007, 2011), a Juris Masters of Law from GMU (2022), and a Graduate Certificate in Project Leadership from Cornell University (2016). He currently holds courtesy appointments with the Center for Advanced Innovation in Agriculture (CAIA), National Security Institute (NSI), and the Department of Electrical and Computer Engineering at VT.

Affiliations and Expertise

Associate Professor, Department of Biological Systems Engineering at Virginia Tech (VT) USA

Laura Freeman

Dr. Laura Freeman is a Research Associate Professor at the Department of Statistics and the Director of the Intelligent Systems Lab at Virginia Tech’s Hume Center. Her research leverages experimental methods for conducting research that brings together cyber-physical systems, Data Science, Artificial Intelligence, and Machine Learning to address critical challenges in national security. She is a CCI fellow.

Affiliations and Expertise

Research Associate Professor, Department of Statistics and Director of the Intelligent Systems Lab, Virginia Tech University, Blacksburg, VA, USA

Ratings and Reviews

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  • Matt K. Fri Nov 04 2022

    Great book... reading on!

    I just purchased this book, and I have been reading through the concepts as they are very interesting and relevant to our recent day tech! Recommended.