An Introduction to Machine Learning Interpretability
Subtitle: An Applied Perspective on Fairness, Accountability, Transparency, and Explainable AI
Edition: Second Edition
Publisher: O'Reilly Media, Inc.
Published: August 2019
Format: Ebook (PDF)
Number of pages: 62
ISBN-13: 9781098115456
Description: Understanding and trusting models and their results is a hallmark of good science. Analysts, engineers, physicians, researchers, scientists, and humans in general have the need to understand and trust models and modeling results that affect our work and our lives. For decades, choosing a model that was transparent to human practitioners or consumers often meant choosing straightforward data sources and simpler model forms such as linear models, single decision trees, or business rule systems. Although these simpler approaches were often the correct choice, and still are today, they can fail in real-world scenarios when the underlying modeled phenomena are nonlinear, rare or faint, or highly specific to certain individuals. Today, the trade-off between the accuracy and interpretability of predictive models has been broken (and maybe it never really existed). The tools now exist to build accurate and sophisticated modeling systems based on heterogeneous data and machine learning algorithms and to enable human understanding and trust in these complex systems.
Notes: This work is part of a collaboration between O’Reilly and H2O but editorially independent.

It is available from the website of H2O:
https://h2o.ai/content/dam/h2o/en/marketing/documents/2019/08/An-Introduction-to-Machine-Learning-Interpretability-Second-Edition.pdf
Table of contents: label: An Introduction to Machine Learning Interpretability
title: 1; label: Definitions and Examples
title: 2; label: Social and Commercial Motivations for Machine Learning Interpretability
title: 5; label: A Machine Learning Interpretability Taxonomy for Applied Practitioners
title: 13; label: Common Interpretability Techniques
title: 17; label: Limitations and Precautions
title: 44; label: Testing Interpretability and Fairness
title: 51; label: Machine Learning Interpretability in Action
title: 53; label: Looking Forward
title: 54