AI Assurance

AI Assurance

Towards Trustworthy, Explainable, Safe, and Ethical AI

1st Edition - October 18, 2022

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

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AI Assurance: Towards Trustworthy, Explainable, Safe, and Ethical AI provides readers with solutions and 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. AI assurance is now a major piece in AI and engineering research, and this book serves as a guide to researchers, scientists, and students in their studies and experimentation with AI. 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 in-depth understanding of how to develop and apply Artificial Intelligence in a valid, explainable, fair, and ethical manner
  • Includes description of providing assurance to 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, cyber security, government, energy, education, and more
  • Provides readers with complete example datasets associated with the methods and algorithms developed in the book


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 muindful 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: 448
  • Language: English
  • Copyright: © Academic Press 2022
  • Published: October 18, 2022
  • Imprint: Academic Press
  • Paperback ISBN: 9780323919197

About the Editors

Feras Batarseh

Feras A. Batarseh is a Teaching Assistant Professor with the Data Analytics Program at Georgetown University, Washington, D.C., and a Research Assistant Professor with the College of Science at George Mason University (GMU), Fairfax, VA. His research and teaching span the areas of Data Science, Artificial Intelligence, and Context-Aware Software Systems. Dr. Batarseh obtained his PhD and MSc in Computer Engineering from the University of Central Florida (UCF) (2007, 2011) and a Graduate Certificate in Project Leadership from Cornell University (2016). His research work has been published at various prestigious journals and international conferences. Additionally, Dr. Batarseh published and edited several book chapters. He is the author and editor of Federal Data Science , another book by Elsevier’s Academic Press. Dr. Batarseh has taught data science and software engineering courses at multiple universities including Georgetown, GMU, UCF, The University of Maryland, Baltimore County (UMBC), as well as George Washington University (GWU).

Affiliations and Expertise

Research Assistant Professor, College of Science, George Mason University, 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

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