Mitigating Bias with Preprocessing

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prerequisites
beginner Python, Jupyter Notebook, and pandas • basic scikit-learn • basics of machine learning
skills learned
setting up a Google Colab environment • AIF360 open-source library • preprocessing for fairness with the reweighing method
Adriano Soares Koshiyama, Umar Mohammed, Emre Kazim, and Catherine Inness
1 week · 5-9 hours per week · INTERMEDIATE
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In this liveProject, you’ll use the open-source AI Fairness 360 toolkit from IBM to measure and mitigate bias with model preprocessing. You will chart bias metrics on a basic classifier, before preprocessing your training data with the “reweighing” method. Reweighing seeks to mitigate bias by making modifications on the training data by computing and applying a set of weights. The weights are calculated such that the training data, with weights applied, is free of discrimination with respect to a protected attribute. Once your model is preprocessed, you’ll construct a classifier that makes use of the data.
This project is a part of the series Mitigate Machine Learning Bias in Mortgage Lending Data.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

prerequisites

The liveProject is for beginner data scientists and software engineers looking to tackle the basic principles of measuring and mitigating ML bias. To begin this liveProject, you will need to be familiar with:

TOOLS
  • Basic Python
  • Basic Jupyter Notebook
  • Basic pandas
  • Basic scikit-learn
  • Basic seaborn
TECHNIQUES
  • Basic machine learning

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