Overview

"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! InterpretableAI is filled with cutting-edge techniques that will improve your understanding of how your AI models function. InterpretableAI 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 opensource libraries. With examples from all major machine learning approaches, this book demonstrates why some approaches to AI are so opaque, teaches you toidentify the patterns your model has learned, and presents best practices for building fair and unbiased models. How deep learning models produce their results is often a complete mystery, even to their creators. These AI""black boxes"" can hide unknown issues—including data leakage, the replication of human bias, and difficulties complying with legal requirements such as the EU's ""right to explanation."" State-of-the-art interpretability techniques have been developed to understand even the most complex deep learning models, allowing humans to follow an AI's methods and to better detect when it has made a mistake."

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9781617297649

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Reviews

“I think this is a valuable book both for beginners as well for more experienced users.”Kim Falk Jørgensen “This book provides a great insight into the interpretability step of developing a structured learning robust AI systems.” IzharHaq “Really great introduction to interpretability of ML models as well asgreat examples of how you can do it to your own models.” JonathanWood “Techniques are consistently presented with excellent examples.” JamesJ. Byleckie “A fine book towards making ML models less opaque.” AlainCouniot “Read this to understand what the model actually says about the underlying data.” Shashank Polasa “Everybody working with ML models should be able to interpret (and check) results. This book will help you with that.” KaiGellien

I think this is a valuable book both for beginners as well for more experienced users. Kim Falk Jorgensen This book provides a great insight into the interpretability step of developing a structured learning robust AI systems. IzharHaq Really great introduction to interpretability of ML models as well asgreat examples of how you can do it to your own models. JonathanWood Techniques are consistently presented with excellent examples. JamesJ. Byleckie A fine book towards making ML models less opaque. AlainCouniot Read this to understand what the model actually says about the underlying data. Shashank Polasa Everybody working with ML models should be able to interpret (and check) results. This book will help you with that. KaiGellien

Author Information

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.

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