Interpretable Machine Learning with Python

Chapter 11: Bias Mitigation and Causal Inference Methods

In Chapter 7, Anchors and Counterfactual Explanations, we examined fairness and its connection to decision-making but limited to post-hoc model interpretation methods. In Chapter 10, Feature Selection and Engineering for Interpretability, we broached the topic of cost-sensitivity, which often relates to balance or fairness. In this chapter, we will engage with methods that will balance data and tune models for fairness.

With a credit card default dataset, we will learn how to leverage target visualizers such as class balance to detect undesired bias, then how to reduce it via preprocessing methods such as reweighting and disparate impact remover for in-processing and equalized odds for ...