Undesired bias in machine learning has become a worrying topic after numerous high-profile incidents have been covered by the media. It’s 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 doesn’t reinvent the wheel, instead choosing to use traditional methods to simplify the issue so it can be tackled from a practical perspective.
Alejandro discusses the high-level definitions of bias in machine learning as two constituent parts—a priori societal bias and a posteriori statistical bias—and uses a hands-on example to demystify it. Your objective is to automate the loan-approval process for a company using machine learning. He walks you through this challenge step-by-step, using key tools and techniques from the latest research, allowing you to assess and mitigate undesired bias in machine learning models. He provides tangible examples of how undesired bias is introduced in each step, introducing interesting research findings in this topic. Spoiler alert: Alejandro takes a pragmatic approach, showing how any nontrivial system will always have an inherent bias; the objective is not to remove bias but to make sure you can get as close as possible to your objectives, and you can make sure your objectives are as close as possible to the ideal solution.
You’ll be introduced to a pragmatic process to assess bias in machine learning models through three key steps: data analysis, inference result analysis, and production metrics analysis and use real-life examples from the automation of a loan-approval process. Some bias may not affect the results in a negative way, and Alejandro explores techniques you can use to ensure you perform a reasonable analysis. His objective isn’t to show how to completely remove bias from a machine learning model but instead to provide you with the tools and techniques available, as well as key touch points and metrics that ensure the right domain experts are involved. He discusses fundamental topics in data science, such as feature importance analysis, class imbalance assessment, model evaluation metrics, partial dependence, feature correlation, etc. More importantly, he takes you through how these fundamentals can interact at different touch points with the right domain experts to ensure undesired bias is identified and documented. You gain practical experience through a Jupyter notebook hands-on example.
Alejandro Saucedo is the chief scientist at the Institute for Ethical AI & Machine Learning. With over 10 years of software development experience, Alejandro has held technical leadership positions across hypergrowth scale-ups and tech giants including Eigen Technologies, Bloomberg LP, and Hack Partners. Alejandro has a strong track record of building multiple departments of machine learning engineers from scratch and leading the delivery of numerous large-scale machine learning systems across the financial, insurance, legal, transport, manufacturing, and construction sectors (in Europe, the US, and Latin America).
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