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240201 ||| eng |
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|a 9781484293065
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|a 1484293061
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4 |
|a Q334.7
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1 |
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|a Duke, Toju
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| 245 |
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|a Building responsible AI algorithms
|b a framework for transparency, fairness, safety, privacy, and robustness
|c Toju Duke
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| 246 |
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|a Building responsible artificial intelligence algorithms
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| 246 |
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|a Building responsible A.I. algorithms
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| 260 |
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|a [New York]
|b Apress
|c 2023
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| 300 |
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|a xvii, 190 pages
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| 505 |
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|a Includes bibliographical references and index
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| 505 |
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|a Introduction -- 1. Responsibility -- 2. AI principles -- 3. Data -- 4. Fairness -- 5. Safety -- 6. Human-in-the-loop -- 7. Explainability -- 8. Privacy -- 9. Robustness -- 10. AI ethics
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| 653 |
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|a Algorithms / http://id.loc.gov/authorities/subjects/sh85003487
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| 653 |
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|a Artificial intelligence / Moral and ethical aspects
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| 653 |
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|a Machine learning / Moral and ethical aspects
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| 653 |
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|a Algorithms / fast / (OCoLC)fst00805020
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| 653 |
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|a Intelligence artificielle / Aspect moral
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| 653 |
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|a Artificial intelligence / Moral and ethical aspects / fast / (OCoLC)fst00817273
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| 653 |
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|a Apprentissage automatique / Aspect moral
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 028 |
5 |
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|a 10.1007/978-1-4842-9306-5
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| 776 |
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|z 1484293061
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| 776 |
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|z 9781484293058
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| 776 |
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|z 1484293053
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| 776 |
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|z 9781484293065
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| 856 |
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|u https://learning.oreilly.com/library/view/~/9781484293065/?ar
|x Verlag
|3 Volltext
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|a 174/.90063
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| 520 |
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|a "This book introduces a Responsible AI framework and guides you through processes to apply at each stage of the machine learning (ML) life cycle, from problem definition to deployment, to reduce and mitigate the risks and harms found in artificial intelligence (AI) technologies. AI offers the ability to solve many problems today if implemented correctly and responsibly. This book helps you avoid negative impacts -- that in some cases have caused loss of life -- and develop models that are fair, transparent, safe, secure, and robust. The approach in this book raises your awareness of the missteps that can lead to negative outcomes in AI technologies and provides a Responsible AI framework to deliver responsible and ethical results in ML. It begins with an examination of the foundational elements of responsibility, principles, and data. Next comes guidance on implementation addressing issues such as fairness, transparency, safety, privacy, and robustness. The book helps you think responsibly while building AI and ML models and guides you through practical steps aimed at delivering responsible ML models, datasets, and products for your end users and customers"--
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