Responsible AI: best practices for creating trustworthy AI systems
AI systems are solving real-world challenges and transforming industries, but there are serious concerns about how responsibly they operate on behalf of the humans that rely on them. Many ethical principles and guidelines have been proposed for AI systems, but they're often too 'high-level...
Gespeichert in:
| Beteiligte Personen: | , , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Boston
Addison-Wesley
[2024]
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| Ausgabe: | [First edition]. |
| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9780138073947/?ar |
| Zusammenfassung: | AI systems are solving real-world challenges and transforming industries, but there are serious concerns about how responsibly they operate on behalf of the humans that rely on them. Many ethical principles and guidelines have been proposed for AI systems, but they're often too 'high-level' to be translated into practice. Conversely, AI/ML researchers often focus on algorithmic solutions that are too 'low-level' to adequately address ethics and responsibility. In this timely, practical guide, pioneering AI practitioners bridge these gaps. The authors illuminate issues of AI responsibility across the entire system lifecycle and all system components, offer concrete and actionable guidance for addressing them, and demonstrate these approaches in three detailed case studies. Writing for technologists, decision-makers, students, users, and other stake-holders, the topics cover: Governance mechanisms at industry, organisation, and team levels Development process perspectives, including software engineering best practices for AI System perspectives, including quality attributes, architecture styles, and patterns Techniques for connecting code with data and models, including key tradeoffs Principle-specific techniques for fairness, privacy, and explainability A preview of the future of responsible AI. |
| Beschreibung: | Includes bibliographical references and index |
| Umfang: | 1 Online-Ressource (320 Seiten) illustrations |
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| spelling | Lu, Qinghua VerfasserIn aut Responsible AI best practices for creating trustworthy AI systems Qinghua Lu, Liming Zhu, Jon Whittle, and Xiwei Xu [First edition]. Boston Addison-Wesley [2024] 1 Online-Ressource (320 Seiten) illustrations Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Includes bibliographical references and index AI systems are solving real-world challenges and transforming industries, but there are serious concerns about how responsibly they operate on behalf of the humans that rely on them. Many ethical principles and guidelines have been proposed for AI systems, but they're often too 'high-level' to be translated into practice. Conversely, AI/ML researchers often focus on algorithmic solutions that are too 'low-level' to adequately address ethics and responsibility. In this timely, practical guide, pioneering AI practitioners bridge these gaps. The authors illuminate issues of AI responsibility across the entire system lifecycle and all system components, offer concrete and actionable guidance for addressing them, and demonstrate these approaches in three detailed case studies. Writing for technologists, decision-makers, students, users, and other stake-holders, the topics cover: Governance mechanisms at industry, organisation, and team levels Development process perspectives, including software engineering best practices for AI System perspectives, including quality attributes, architecture styles, and patterns Techniques for connecting code with data and models, including key tradeoffs Principle-specific techniques for fairness, privacy, and explainability A preview of the future of responsible AI. Artificial intelligence Moral and ethical aspects Intelligence artificielle ; Aspect moral Zhu, Liming 1975- VerfasserIn aut Whittle, Jon 1972- VerfasserIn aut Xu, Xiwei VerfasserIn aut |
| spellingShingle | Lu, Qinghua Zhu, Liming 1975- Whittle, Jon 1972- Xu, Xiwei Responsible AI best practices for creating trustworthy AI systems Artificial intelligence Moral and ethical aspects Intelligence artificielle ; Aspect moral |
| title | Responsible AI best practices for creating trustworthy AI systems |
| title_auth | Responsible AI best practices for creating trustworthy AI systems |
| title_exact_search | Responsible AI best practices for creating trustworthy AI systems |
| title_full | Responsible AI best practices for creating trustworthy AI systems Qinghua Lu, Liming Zhu, Jon Whittle, and Xiwei Xu |
| title_fullStr | Responsible AI best practices for creating trustworthy AI systems Qinghua Lu, Liming Zhu, Jon Whittle, and Xiwei Xu |
| title_full_unstemmed | Responsible AI best practices for creating trustworthy AI systems Qinghua Lu, Liming Zhu, Jon Whittle, and Xiwei Xu |
| title_short | Responsible AI |
| title_sort | responsible ai best practices for creating trustworthy ai systems |
| title_sub | best practices for creating trustworthy AI systems |
| topic | Artificial intelligence Moral and ethical aspects Intelligence artificielle ; Aspect moral |
| topic_facet | Artificial intelligence Moral and ethical aspects Intelligence artificielle ; Aspect moral |
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