LEADER 00000nam a22003977i 4500 
003    OCoLC 
005    20240129213017.0 
006    m     o  d         
007    cr cnu|||unuuu 
008    231206s2024    njua    ob    001 0 eng d 
035    (OCoLC)1412007594 
037    9780138073947|bO'Reilly Media 
040    ORMDA|beng|erda|epn|cORMDA|dOCLCO 
049    INap 
082 04 006.301 
082 04 006.301|223/eng/20231206 
099    eBook O'Reilly for Public Libraries 
100 1  Lu, Qinghua,|eauthor. 
245 10 Responsible AI :|bbest practices for creating trustworthy 
       AI systems /|cQinghua Lu, Liming Zhu, Jon Whittle, and 
       Xiwei Xu.|h[O'Reilly electronic resource] 
246 3  Responsible artificial intelligence 
250    [First edition]. 
264  1 Boston :|bAddison-Wesley,|c[2024] 
300    1 online resource (320 pages) :|billustrations 
336    text|btxt|2rdacontent 
337    computer|bc|2rdamedia 
338    online resource|bcr|2rdacarrier 
504    Includes bibliographical references and index. 
520    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. 
590    O'Reilly|bO'Reilly Online Learning: Academic/Public 
       Library Edition 
650  0 Artificial intelligence|xMoral and ethical aspects. 
650  6 Intelligence artificielle|xAspect moral. 
700 1  Zhu, Liming,|d1975-|eauthor. 
700 1  Whittle, Jon,|d1972-|eauthor. 
700 1  Xu, Xiwei,|eauthor. 
856 40 |uhttps://ezproxy.naperville-lib.org/login?url=https://
       learning.oreilly.com/library/view/~/9780138073947/?ar
       |zAvailable on O'Reilly for Public Libraries 
994    92|bJFN