Mitigate Machine Learning Bias: A Health Data Project you own this product

prerequisites
intermediate Python • basics of pandas • basics of scikit-learn
skills learned
biasing and debiasing ML models • using SHAP to interpret the underlying logic behind a ML model • using AIF360 to detect and mitigate unwanted bias • auditing techniques for unknown at-risk populations
Mike McKenna
4 weeks · 6-8 hours per week · INTERMEDIATE
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Unexpected bias in machine learning models reduces accuracy, produces negative real-world consequences, and in the worst cases, entrenches existing inequalities for decades. Audits that can detect and mitigate this unwanted bias are an essential part of building and maintaining reliable machine learning systems.

In this liveProject, you’ll take on the role of a data scientist running a bias audit for the World Health Organization’s healthcare models. You’ll get to grips with state-of-the-art tools and best practices for bias detection and mitigation, including interpretability methods, the AIF-360 package, and options for imputing membership of a protected class. These tools and practices are placed in the context of a broad universe of bias where geopolitical awareness and deep understanding of the history and use of your particular data is key.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Mike McKenna
Michael McKenna is a senior data scientist at CVS Health. An expert in algorithmic bias within artificial intelligence, he combines his engineering and legal background to contribute to policymaking at the highest levels of American healthcare. All opinions expressed are his own and do not necessarily reflect the position of CVS Health.

prerequisites

The liveProject is for intermediate Python programmers with experience in data science. To begin this liveProject you need to be familiar with:

TOOLS
  • Basics of pandas
  • Basics of scikit-learn
  • Basics of Jupyter Notebook

TECHNIQUES
  • Classification and regression using random forests and gradient boosting machines

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