AI assurance : towards trustworthy, explainable, safe, and ethical AI / edited by Feras A. Batarseh, Laura J. Freeman.
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 de...
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| Format: | Ebook |
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| Language: | English |
| Published: |
London, United Kingdom ; San Diego, CA :
Academic Press, an imprint of Elsevier,
[2023]
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| Subjects: | |
| Online Access: | Click here to view this book |
| Summary: | 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. |
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| Item Description: | 1. An introduction to AI assurance<br>2. Setting the goals for ethical, unbiased and fair AI<br>3. An overview of explainable and interpretable AI<br>4. Bias, Fairness, and assurance in AI: Overview and Synthesis<br>5. An evaluation of the potential global impacts of AI assurance<br>6. The role of inference in AI: start S.M.A.L.L. with muindful models<br>7. Outlier detection using AI: a survey<br>8. AI assurance using casual inference: application to public policy<br>9. Data collection, wrangling and preprocessing for AI assurance<br>10. Coordination-aware assurance for end-to-end machine learning systems: the R3E approach<br>11. Assuring AI methods for economic policymaking<br>12. Panopticon implications of ethical AI: equity, disparity, and inequality in healthcare<br>13. Recent advances in uncertainty quantification methods for engineering problems<br>14. Socially responsible AI assurance in precision agriculture for farmers and policymakers <br>15. The application of AI assurance in precision farming and agricultural economics <br>16. Bringing dark data to light with AI for evidence-based policy making. |
| Physical Description: | 1 online resource (xxxii, 568 pages) : illustrations |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: | 0323918824 9780323918824 |