| Description |
1 online resource (xxii, 568 pages) : illustrations |
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text txt rdacontent |
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computer c rdamedia |
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online resource cr rdacarrier |
| Bibliography |
Includes bibliographic references and index. |
| 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 |
| Note |
Description based on online resource; title from PDF title page (Science Direct, viewed January 2, 2024). |
| 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. |
| Subject |
Artificial intelligence.
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Intelligence artificielle.
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artificial intelligence.
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Artificial intelligence
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| Added Author |
Batarseh, Feras, editor.
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Freeman, Laura June, editor.
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| Other Form: |
Print version: AI assurance. London, United Kingdom ; San Diego, CA : Academic Press, an imprint of Elsevier, [2023] 9780323919197 (OCoLC)1330197839 |
| ISBN |
9780323919197 (electronic bk.) |
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0323919197 (electronic bk.) |
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9780323918824 (electronic bk.) |
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0323918824 (electronic bk.) |
| Standard No. |
UKMGB 020702646 |
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AU@ 000073012444 |
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