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Manipulating and Measuring Model Interpretability /
"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 enou...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor Corporativo: | |
| Formato: | Electrónico Congresos, conferencias Video |
| Idioma: | Inglés |
| Publicado: |
[Place of publication not identified] :
O'Reilly Media,
2019.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Poursabzi-Sangdeh, Forough, |e on-screen presenter. | |
| 245 | 1 | 0 | |a Manipulating and Measuring Model Interpretability / |c Forough Poursabzi-Sangdeh. |
| 264 | 1 | |a [Place of publication not identified] : |b O'Reilly Media, |c 2019. | |
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| 500 | |a Title from resource description page (Safari, viewed November 12, 2019). | ||
| 511 | 0 | |a Presenter, Forough Poursabzi-Sangdeh. | |
| 520 | |a "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 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
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