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.
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.
- Experience with a machine learning project
What you'll learn
- Gain an overview of the concept of bias in machine learning
- Learn the three key steps to assess bias throughout the lifecycle of a machine learning model
- Understand how key machine learning concepts, such as feature importance, class imbalance, model analysis, partial dependence, etc., are used in a practical example, as well as how these data science fundamentals can be used to interact with key domain experts
This session is from the 2019 O'Reilly Artificial Intelligence Conference in San Jose, CA.
- Title: A practical guide toward explainability and bias evaluation in AI and machine learning
- Release date: February 2020
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 0636920371243
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