Interpretable AI: Building explainable machine learning systems PDF
Preview Interpretable AI: Building explainable machine learning systems
Building explainable machine learning systems Ajay Thampi M A N N I N G Historical data Training and cross-validation Training and Model Validation 1 LEARNING dev sets Development 2 TESTING Test set Learned model Evaluation Interpretation 3 UNDERSTANDING Test Deployed 4 DEPLOYING New data Prediction model 5 EXPLAINING Interpretation Explanation 6 MONITORING Monitoring Production The process of building a robust AI system Interpretable AI Interpretable AI B UILDING EXPLAINABLE MACHINE LEARNING SYSTEMS AJAY THAMPI MANNING SHELTER ISLAND For online information and ordering of this and other Manning books, please visit www.manning.com. The publisher offers discounts on this book when ordered in quantity. For more information, please contact Special Sales Department Manning Publications Co. 20 Baldwin Road PO Box 761 Shelter Island, NY 11964 Email: [email protected] ©2022 by Manning Publications Co. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by means electronic, mechanical, photocopying, or otherwise, without prior written permission of the publisher. Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in the book, and Manning Publications was aware of a trademark claim, the designations have been printed in initial caps or all caps. Recognizing the importance of preserving what has been written, it is Manning’s policy to have the books we publish printed on acid-free paper, and we exert our best efforts to that end. Recognizing also our responsibility to conserve the resources of our planet, Manning books are printed on paper that is at least 15 percent recycled and processed without the use of elemental chlorine. The author and publisher have made every effort to ensure that the information in this book was correct at press time. The author and publisher do not assume and hereby disclaim any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from negligence, accident, or any other cause, or from any usage of the information herein. Manning Publications Co. Development editor: Lesley Trites 20 Baldwin Road Technical development editor: Kostas Passadis PO Box 761 Review editor: Mihaela Batinić Shelter Island, NY 11964 Production editor: Deirdre Hiam Copy editor: Pamela Hunt Proofreader: Melody Dolab Technical proofreader: Vishwesh Ravi Shrimali Typesetter: Gordan Salinovic Cover designer: Marija Tudor ISBN 9781617297649 Printed in the United States of America To Achan, Amma, Ammu, and my dear Miru brief content P 1 I ...................................................1 ART NTERPRETABILITY BASICS 1 ■ Introduction 3 2 ■ White-box models 21 P 2 I ....................................55 ART NTERPRETING MODEL PROCESSING 3 ■ Model-agnostic methods: Global interpretability 57 4 ■ Model-agnostic methods: Local interpretability 89 5 ■ Saliency mapping 126 P 3 I ..........................165 ART NTERPRETING MODEL REPRESENTATIONS 6 ■ Understanding layers and units 167 7 ■ Understanding semantic similarity 200 P 4 F ........................................................235 ART AIRNESS AND BIAS 8 ■ Fairness and mitigating bias 237 9 ■ Path to explainable AI 270 vii
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