A practical guide to algorithmic bias and explainability in machine learning
"The concepts of "undesired bias" and "black box models" in machine learning have become a highly discussed topic due to the numerous high profile incidents that have been covered by the media. It's certainly a challenging topic, as it could even be said that the concep...
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| Format: | Online Video |
| Language: | Inglés |
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[Place of publication not identified] :
O'Reilly Media
2020.
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| See on Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009822819506719 |
| Summary: | "The concepts of "undesired bias" and "black box models" in machine learning have become a highly discussed topic due to the numerous high profile incidents that have been covered by the media. It's certainly a challenging topic, as it could even be said that the concept of societal bias is inherently biased in itself, depending on an individual's (or group's) perspective. 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."--Resource description page. |
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| Item Description: | Title from resource description page (viewed July 23, 2020). This session is from the 2019 O'Reilly Strata Conference in New York, NY. |
| Physical Description: | 1 online resource (1 streaming video file (40 min., 48 sec.)) : digital, sound, color |
