02052nam a22002537a 4500 20260609125819.0 260609s2024 |||||||| |||| 00| 0 eng d 9780138073923 PK-LaUMT 006.3 RES- Responsible AI : best practices for creating trustworthy AI systems / Qinghua Lu . . . [et al.] Boston : Addison-Wesley, 2024 xix, 291 p. Index present 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 Eng Artificial intelligence Artificial intelligence-social aspects 8102 Lu, Qinghua 10181 Zhu, Liming 10182 Whittle, Jon 10183 Xu, Xiwei 10184 BK 141064 141064 0 0 0 0 01 01 2026-06-09 0 006.3 RES- 152896 2026-06-09 12:58:29 2026-06-09 BK 0 0 0 0 01 01 2026-07-20 0 006.3 RES- 153518 2026-07-20 16:09:13 C.2 2026-07-20 BK