LEADER 00000cgm a2200469 i 4500 
003    OCoLC 
005    20240129213017.0 
006    m     o  c         
007    cr cna|||||||| 
007    vz czazuu 
008    191114s2019    xx 040        o   vleng d 
029 1  AU@|b000066431084 
035    (OCoLC)1127579404 
037    CL0501000082|bSafari Books Online 
040    UMI|beng|erda|epn|cUMI|dOCLCF|dTOH|dOCLCO|dOCLCQ|dOCLCO 
049    INap 
099    Streaming Video O’Reilly for Public Libraries 
100 1  Poursabzi-Sangdeh, Forough,|eon-screen presenter. 
245 10 Manipulating and Measuring Model Interpretability /
       |cForough Poursabzi-Sangdeh.|h[O'Reilly electronic 
       resource] 
264  1 [Place of publication not identified] :|bO'Reilly Media,
       |c2019. 
300    1 online resource (1 streaming video file (39 min., 42 
       sec.)) 
336    two-dimensional moving image|btdi|2rdacontent 
337    computer|bc|2rdamedia 
337    video|bv|2rdamedia 
338    online resource|bcr|2rdacarrier 
500    Title from resource description page (Safari, viewed 
       November 12, 2019). 
511 0  Presenter, Forough Poursabzi-Sangdeh. 
520    "Machine learning is increasingly used to make decisions 
       that affect people's lives in critical domains like 
       criminal justice, fair lending, and medicine. While most 
       of the research in machine learning focuses on improving 
       the performance of models on held-out datasets, this is 
       seldom enough to convince end users that these models are 
       trustworthy and reliable in the wild. To address this 
       problem, a new line of research has emerged that focuses 
       on developing interpretable machine learning methods and 
       helping end users make informed decisions. Despite the 
       growing body of work in developing interpretable models, 
       there is still no consensus on the definition and 
       quantification of interpretability ... Forough approaches 
       the problem of interpretability from an interdisciplinary 
       perspective built on decades of research in psychology, 
       cognitive science, and social science to understand human 
       behavior and trust. She describes a set of controlled user
       experiments in which researchers manipulated various 
       design factors in models that are commonly thought to make
       them more or less interpretable and measured their 
       influence on users' behavior."--Resource description page 
590    O'Reilly|bO'Reilly Online Learning: Academic/Public 
       Library Edition 
650  0 Machine learning. 
650  0 Artificial intelligence. 
650  2 Artificial Intelligence 
650  6 Apprentissage automatique. 
650  6 Intelligence artificielle. 
650  7 artificial intelligence.|2aat 
650  7 Artificial intelligence.|2fast|0(OCoLC)fst00817247 
650  7 Machine learning.|2fast|0(OCoLC)fst01004795 
655  4 Electronic videos. 
711 2  O'Reilly Artificial Intelligence Conference|d(15-18 April 
       2019 :|cNew York, N.Y.)|jissuing body. 
856 40 |uhttps://ezproxy.naperville-lib.org/login?url=https://
       learning.oreilly.com/videos/~/0636920339724/?ar|zAvailable
       for O'Reilly for Public Libraries 
994    92|bJFN