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A practical guide toward explainability and bias evaluation in AI and machine learning /
"Alejandro Saucedo (The Institute for Ethical AI & Machine Learning) doesn't reinvent the wheel; he simplifies the issue of AI explainability so it can be solved using traditional methods. He covers the high-level definitions of bias in machine learning to remove ambiguity and demystif...
| Clasificación: | Libro Electrónico |
|---|---|
| Formato: | Electrónico 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 Saucedo, Alejandro, |e on-screen presenter. | |
| 245 | 1 | 2 | |a A practical guide toward explainability and bias evaluation in AI and machine learning / |c Alejandro Saucedo. |
| 264 | 1 | |a [Place of publication not identified] : |b O'Reilly Media, |c 2019. | |
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| 511 | 0 | |a Presenter, Alejandro Saucedo. | |
| 500 | |a Title from title screen (viewed July 23, 2020). | ||
| 520 | |a "Alejandro Saucedo (The Institute for Ethical AI & Machine Learning) doesn't reinvent the wheel; he simplifies the issue of AI explainability so it can be solved using traditional methods. He covers the high-level definitions of bias in machine learning to remove ambiguity and demystifies it through a hands-on example, in which the objective is to automate the loan-approval process for a company using machine learning, which allows you to go through this challenge step by step and use key tools and techniques from the latest research together with domain expert knowledge at the right points to enable you to explain decisions and mitigate undesired bias in machine learning models. Alejandro breaks undesired bias down into two constituent parts: a priori societal bias and a posteriori statistical bias, with tangible examples of how undesired bias is introduced in each step, and you'll learn some very interesting research findings in this topic. Spoiler alert: Alejandro takes a pragmatic approach, showing how any nontrivial system will always have an inherent bias, so the objective is not to remove bias, but to make sure you can get as close as possible to your objectives and make sure your objectives are as close as possible to the ideal solution. This session is from the 2019 O'Reilly Artificial Intelligence Conference in San Jose, CA."--Resource description page | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 611 | 2 | 0 | |a O'Reilly Artificial Intelligence Conference |d (2019 : |c San Jose, Calif.) |
| 650 | 0 | |a Artificial intelligence |x Moral and ethical aspects. | |
| 650 | 0 | |a Machine learning |x Moral and ethical aspects. | |
| 650 | 6 | |a Intelligence artificielle |x Aspect moral. | |
| 650 | 6 | |a Apprentissage automatique |x Aspect moral. | |
| 650 | 7 | |a Artificial intelligence |x Moral and ethical aspects |2 fast |0 (OCoLC)fst00817273 | |
| 856 | 4 | 0 | |u https://learning.oreilly.com/videos/~/0636920371267/?ar |z Texto completo (Requiere registro previo con correo institucional) |
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