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Deep Learning and XAI Techniques for Anomaly Detection

You're reading from Deep Learning and XAI Techniques for Anomaly Detection

Product type Book

Published in Jan 2023

Publisher Packt

ISBN-13 9781804617755

Pages 218 pages

Edition 1st Edition

Languages

Concepts

Author (1):

Cher Simon

Table of Contents (15) Chapters

Preface

1. Part 1 – Introduction to Explainable Deep Learning Anomaly Detection

2. Chapter 1: Understanding Deep Learning Anomaly Detection

3. Chapter 2: Understanding Explainable AI

4. Part 2 – Building an Explainable Deep Learning Anomaly Detector

5. Chapter 3: Natural Language Processing Anomaly Explainability

6. Chapter 4: Time Series Anomaly Explainability

7. Chapter 5: Computer Vision Anomaly Explainability

8. Part 3 – Evaluating an Explainable Deep Learning Anomaly Detector

9. Chapter 6: Differentiating Intrinsic and Post Hoc Explainability

10. Chapter 7: Backpropagation versus Perturbation Explainability

11. Chapter 8: Model-Agnostic versus Model-Specific Explainability

12. Chapter 9: Explainability Evaluation Schemes

13. Index

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14. Other Books You May Enjoy

Reviewing the System Causability Scale (SCS)

Created in 1986 by John Brooke, the System Usability Scale (SUS) provides a simple tool to measure the usability of a system’s user interface. It consists of a 10-item questionnaire with 5 response options, allowing respondents to evaluate the quality of products and services from strongly agree to strongly disagree.

Inspired by SUS, SCS, available at https://doi.org/10.1007/s13218-020-00636-z, focuses on measuring the qualitative aspect of XAI systems. SCS meets the XAI principle of meaningfulness by NIST to determine end user perception against explanations provided by XAI systems.

SCS assesses the effectiveness of an XAI user interface in providing explanations to the target audience using a Likert scale questionnaire covering the following scopes:

A given explanation offers accurate causal factors with relevant granularity
The context of a given explanation makes sense to the user
Users can tune the level...

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Authors (1)

Cher Simon

Cher Simon is a principal solutions architect specializing in artificial intelligence, machine learning, and data analytics at AWS. Cher has 20 years of experience in architecting enterprise-scale, data-driven, and AI-powered industry solutions. Besides building cloud-native solutions in her day-to-day role with customers, Cher is also an avid writer and a frequent speaker at AWS conferences.

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