000			02052nam a22002537a 4500
005			20260609125819.0
008			260609s2024 |||||||| |||| 00| 0 eng d
020			_a9780138073923
040			_cPK-LaUMT
082			_a006.3 _bRES-
245	0	0	_aResponsible AI : _bbest practices for creating trustworthy AI systems / _cQinghua Lu . . . [et al.]
260			_aBoston : _bAddison-Wesley, _c2024
300			_axix, 291 p.
500			_aIndex present
520			_aAI 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
546			_aEng
650			_aArtificial intelligence
650			_aArtificial intelligence-social aspects _98102
700	1		_aLu, Qinghua _910181
700	1		_aZhu, Liming _910182
700	1		_aWhittle, Jon _910183
700	1		_aXu, Xiwei _910184
942			_cBK
999			_c141064 _d141064