AI Assurance
Towards Trustworthy, Explainable, Safe, and Ethical AI
Published on 17. October 2022
Book
Paperback/Softback
600 pages
978-0-323-91919-7 (ISBN)
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.
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.
Reviews / Votes
"The book's structure allows readers to appreciate the interconnectedness of the various aspects of AI assurance. The editors have thoughtfully curated content that demonstrates the intricate relationship between technical, ethical, and practical considerations. The chapters build upon one another, providing a comprehensive understanding of AI assurance while simultaneously allowing readers to explore specific topics in greater depth. One of the book's most striking features is its commitment to providing practical, real-world examples to illustrate the concepts discussed in each chapter.... a captivating scholarly book that offers a thought-provoking and comprehensive examination of AI assurance. We highly recommend this book to scholars, policymakers, industry practitioners, and anyone seeking to navigate the complex labyrinth of AI assurance. [It] has the potential to shape the future of AI development and implementation, ultimately ensuring a more ethical, safe, and beneficial integration of AI into our society." - Jialei Wang (Shining3D Tech Co.) and Li Fu (Hangzhou Dianzi University), AI & Society, November 2023More details
Language
English
Place of publication
Oxford
United Kingdom
Publishing group
Elsevier Science & Technology
Target group
Professional and scholarly
Product notice
Paperback (trade)
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 31 mm
Weight
1021 gr
ISBN-13
978-0-323-91919-7 (9780323919197)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Feras A. Batarseh | Laura Freeman
AI Assurance
Towards Trustworthy, Explainable, Safe, and Ethical AI
E-Book
10/2022
Academic Press
€148.00
Available for download
Persons
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. 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.
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. 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.
Content
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
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