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Explainable Artificial Intelligence

Overview of attention for book
Cover of 'Explainable Artificial Intelligence'

Table of Contents

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    Book Overview
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    Chapter 1 Opening the Black Box: Analyzing Attention Weights and Hidden States in Pre-trained Language Models for Non-language Tasks
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    Chapter 2 Evaluating Self-attention Interpretability Through Human-Grounded Experimental Protocol
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    Chapter 3 Understanding Interpretability: Explainable AI Approaches for Hate Speech Classifiers
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    Chapter 4 From Black Boxes to Conversations: Incorporating XAI in a Conversational Agent
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    Chapter 5 Toward Inclusive Online Environments: Counterfactual-Inspired XAI for Detecting and Interpreting Hateful and Offensive Tweets
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    Chapter 6 Causal-Based Spatio-Temporal Graph Neural Networks for Industrial Internet of Things Multivariate Time Series Forecasting
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    Chapter 7 Investigating the Effect of Pre-processing Methods on Model Decision-Making in EEG-Based Person Identification
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    Chapter 8 State Graph Based Explanation Approach for Black-Box Time Series Model
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    Chapter 9 A Deep Dive into Perturbations as Evaluation Technique for Time Series XAI
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    Chapter 10 Towards a Comprehensive Human-Centred Evaluation Framework for Explainable AI
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    Chapter 11 Development of a Human-Centred Psychometric Test for the Evaluation of Explanations Produced by XAI Methods
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    Chapter 12 Concept Distillation in Graph Neural Networks
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    Chapter 13 Adding Why to What? Analyses of an Everyday Explanation
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    Chapter 14 For Better or Worse: The Impact of Counterfactual Explanations’ Directionality on User Behavior in xAI
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    Chapter 15 The Importance of Distrust in AI
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    Chapter 16 Weighted Mutual Information for Out-Of-Distribution Detection
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    Chapter 17 Leveraging Group Contrastive Explanations for Handling Fairness
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    Chapter 18 LUCID–GAN: Conditional Generative Models to Locate Unfairness
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    Chapter 19 Explainable Machine Learning via Argumentation
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    Chapter 20 A Novel Structured Argumentation Framework for Improved Explainability of Classification Tasks
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    Chapter 21 Hardness of Deceptive Certificate Selection
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    Chapter 22 Integrating GPT-Technologies with Decision Models for Explainability
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    Chapter 23 Outcome-Guided Counterfactuals from a Jointly Trained Generative Latent Space
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    Chapter 24 An Exploration of the Latent Space of a Convolutional Variational Autoencoder for the Generation of Musical Instrument Tones
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    Chapter 25 Improving Local Fidelity of LIME by CVAE
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    Chapter 26 Scalable Concept Extraction in Industry 4.0
Attention for Chapter 14: For Better or Worse: The Impact of Counterfactual Explanations’ Directionality on User Behavior in xAI
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Chapter title
For Better or Worse: The Impact of Counterfactual Explanations’ Directionality on User Behavior in xAI
Chapter number 14
Book title
Explainable Artificial Intelligence
Published by
Springer, Cham, January 2023
DOI 10.1007/978-3-031-44070-0_14
Book ISBNs
978-3-03-144069-4, 978-3-03-144070-0
Authors

Kuhl, Ulrike, Artelt, André, Hammer, Barbara

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X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 33%
Lecturer 1 33%
Unknown 1 33%
Readers by discipline Count As %
Computer Science 2 67%
Pharmacology, Toxicology and Pharmaceutical Science 1 33%

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