Description
Edited by Batarseh, Feras A.; Edited by Freeman Laura
:
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.
Table of contents:
1. An introduction to AI assurance2. Setting the goals for ethical, unbiased and fair AI3. An overview of explainable and interpretable AI4. Bias, Fairness, and assurance in AI: Overview and Synthesis5. An evaluation of the potential global impacts of AI assurance6. The role of inference in AI: start S.M.A.L.L. with mindful models7. Outlier detection using AI: a survey8. AI assurance using casual inference: application to public policy9. Data collection, wrangling and preprocessing for AI assurance10. Coordination-aware assurance for end-to-end machine learning systems: the R3E approach11. Assuring AI methods for economic policymaking12. Panopticon implications of ethical AI: equity, disparity, and inequality in healthcare13. Recent advances in uncertainty quantification methods for engineering problems14. 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
Review quote:
“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 2023
Biographical note:
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
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