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Interpretable AI

Published by Manning
Distributed by Simon & Schuster

Interpretable AI is a hands-on guide to interpretability techniques that open up the black box of AI.

AI models can become so complex that even experts have difficulty understanding them—and forget about explaining the nuances of a cluster of novel algorithms to a business stakeholder! Interpretable AI is filled with cutting-edge techniques that will improve your understanding of how your AI models function.

Interpretable AI is a hands-on guide to interpretability techniques that open up the black box of AI. This practical guide simplifies cutting-edge research into transparent and explainable AI, delivering practical methods you can easily implement with Python and open source libraries. With examples from all major machine learning approaches, this book demonstrates why some approaches to AI are so opaque, teaches you to identify the patterns your model has learned, and presents best practices for building fair and unbiased models.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

Ajay Thampi is a machine learning engineer at a large tech company primarily focused on responsible AI and fairness. He holds a PhD and his research was focused on signal processing and machine learning. He has published papers at leading conferences and journals on reinforcement learning, convex optimization, and classical machine learning techniques applied to 5G cellular networks.