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Interpretable Machine Learning with Python - Second Edition

You're reading from  Interpretable Machine Learning with Python - Second Edition

Product type Book
Published in Oct 2023
Publisher Packt
ISBN-13 9781803235424
Pages 606 pages
Edition 2nd Edition
Languages
Author (1):
Serg Masís Serg Masís
Profile icon Serg Masís

Table of Contents (17) Chapters

Preface 1. Interpretation, Interpretability, and Explainability; and Why Does It All Matter? 2. Key Concepts of Interpretability 3. Interpretation Challenges 4. Global Model-Agnostic Interpretation Methods 5. Local Model-Agnostic Interpretation Methods 6. Anchors and Counterfactual Explanations 7. Visualizing Convolutional Neural Networks 8. Interpreting NLP Transformers 9. Interpretation Methods for Multivariate Forecasting and Sensitivity Analysis 10. Feature Selection and Engineering for Interpretability 11. Bias Mitigation and Causal Inference Methods 12. Monotonic Constraints and Model Tuning for Interpretability 13. Adversarial Robustness 14. What’s Next for Machine Learning Interpretability? 15. Other Books You May Enjoy
16. Index

Mission accomplished

This chapter’s mission was to see whether there was unfair bias in predicting whether a particular defendant would recidivate. We demonstrated that the FPR for African American defendants is 1.87 times higher than for Caucasian defendants. This disparity was confirmed with WIT, indicating that the model in question is much more likely to misclassify the positive class based on race. However, this is a global interpretation method, so it doesn’t answer our question regarding a specific defendant. Incidentally, in Chapter 11, Bias Mitigation and Causal Inference Methods, we will cover other global interpretation methods for unfairness.

To ascertain whether the model was racially biased against the defendant in question, we leveraged anchor and counterfactual explanations – they both output race as a primary feature in their explanations. The anchor did it with relatively high precision and coverage, and Counterfactuals Guided by Prototypes...

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