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001			23282485
003			OSt
005			20260209174628.0
008			230202t20242024maua e b 001 0 eng d
010			_a 2023915722
020			_a9780138073923 _q(paperback)
020			_a0138073929 _q(paperback)
035			_a(OCoLC)1365050261
035			_a23282485
040			_aYDX _beng _cTUPM _dBDX _dMOS _dOCLCO _dYDX _dOCLCQ _dHF9 _dOCLCO _dIUL _dOCLCL _dLMR _dDLC _erda
042			_alccopycat
050	0	0	_aQ334.5 _bL83 2024
100	1		_aLu, Qinghua, _eauthor.
245	1	0	_aResponsible AI : _bbest practices for creating trustworthy AI systems / _cQinghua Lu, Liming Zhu, Jon Whittle, and Xiwei Xu.
246	3		_aResponsible artificial intelligence
264		1	_aBoston : _bAddison-Wesley, _c[2024]
264		4	_c©2024
300			_axix, 291 pages : _billustrations ; _c23 cm
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
504			_aIncludes bibliographical references and index.
505	0		_aPreface -- About the author -- Part I. Background and introduction: 1. Introduction to responsible AI ; 2. Operationalizing responsible AI : A thought Experiment - Robbie the Robot -- Part II.: Responsible AI Pattern Catalogue:- 3. Overview of the Responsible AI Pattern Catalogue ; 4. Multi-Level Governance Patterns for Responsible AI ; 5. Process Patterns for Trustworthy Development Processes ; 6. Product Patterns for Responsible-AI-by-Design ; 7. Pattern-Oriented Reference Architecture for Responsible-AI-by-Design ; 8. Principle-Specific Techniques for Responsible AI -- Part III.: Case Studies: 9. Risk-Based AI Governance in Telstra ; 10. Reejig : The World's First Independently Audited Ethical Talent AI ; 11. Diversity and Inclusion in Artificial Intelligence -- Part IV. Looking to the Future: 12. The Future of Responsible AI ; Part V : Appendix -- Index.
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. -- Provided by publisher.
590			_aLu, Q., Zhu, L., Whittle, J., & Xu, X. (2024). Responsible AI: Best practices for creating trustworthy AI systems. Addison-Wesley.
650		0	_aArtificial intelligence _xMoral and ethical aspects.
650		0	_aArtificial intelligence.
650		0	_aMachine learning.
650		6	_aIntelligence artificielle _xAspect moral.
650		6	_aIntelligence artificielle.
650		6	_aApprentissage automatique.
650		7	_aartificial intelligence. _2aat
700	1		_aZhu, Liming, _d1975- _eauthor.
700	1		_aWhittle, Jon, _d1972- _eauthor.
700	1		_aXu, Xiwei, _eauthor.
906			_a7 _bcbc _corignew _d2 _eepcn _f20 _gy-gencatlg
942			_2lcc _cBK _n0
999			_c31254 _d31254