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XxAI - Beyond Explainable AI : International Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Papers.

Bibliographic Details
Main Author: Holzinger, Andreas.
Other Authors: Goebel, Randy., Fong, Ruth., Moon, Taesup., Müller, Klaus-Robert., Samek, Wojciech.
Format: eBook
Language:English
Published: Cham : Springer International Publishing AG, 2022.
Edition:1st ed.
Series:Lecture Notes in Computer Science Series
Subjects:
Electronic books.
Online Access:Click to View
Table of Contents:
  • Intro
  • Preface
  • Organization
  • Contents
  • Editorial
  • xxAI - Beyond Explainable Artificial Intelligence
  • 1 Introduction and Motivation for Explainable AI
  • 2 Explainable AI: Past and Present
  • 3 Book Structure
  • References
  • Current Methods and Challenges
  • Explainable AI Methods - A Brief Overview
  • 1 Introduction
  • 2 Explainable AI Methods - Overview
  • 2.1 LIME (Local Interpretable Model Agnostic Explanations)
  • 2.2 Anchors
  • 2.3 GraphLIME
  • 2.4 Method: LRP (Layer-wise Relevance Propagation)
  • 2.5 Deep Taylor Decomposition (DTD)
  • 2.6 Prediction Difference Analysis (PDA)
  • 2.7 TCAV (Testing with Concept Activation Vectors)
  • 2.8 XGNN (Explainable Graph Neural Networks)
  • 2.9 SHAP (Shapley Values)
  • 2.10 Asymmetric Shapley Values (ASV)
  • 2.11 Break-Down
  • 2.12 Shapley Flow
  • 2.13 Textual Explanations of Visual Models
  • 2.14 Integrated Gradients
  • 2.15 Causal Models
  • 2.16 Meaningful Perturbations
  • 2.17 EXplainable Neural-Symbolic Learning (X-NeSyL)
  • 3 Conclusion and Future Outlook
  • References
  • General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models
  • 1 Introduction
  • 2 Assuming One-Fits-All Interpretability
  • 3 Bad Model Generalization
  • 4 Unnecessary Use of Complex Models
  • 5 Ignoring Feature Dependence
  • 5.1 Interpretation with Extrapolation
  • 5.2 Confusing Linear Correlation with General Dependence
  • 5.3 Misunderstanding Conditional Interpretation
  • 6 Misleading Interpretations Due to Feature Interactions
  • 6.1 Misleading Feature Effects Due to Aggregation
  • 6.2 Failing to Separate Main from Interaction Effects
  • 7 Ignoring Model and Approximation Uncertainty
  • 8 Ignoring the Rashomon Effect
  • 9 Failure to Scale to High-Dimensional Settings
  • 9.1 Human-Intelligibility of High-Dimensional IML Output
  • 9.2 Computational Effort.
  • 9.3 Ignoring Multiple Comparison Problem
  • 10 Unjustified Causal Interpretation
  • 11 Discussion
  • References
  • CLEVR-X: A Visual Reasoning Dataset for Natural Language Explanations
  • 1 Introduction
  • 2 Related Work
  • 3 The CLEVR-X Dataset
  • 3.1 The CLEVR Dataset
  • 3.2 Dataset Generation
  • 3.3 Dataset Analysis
  • 3.4 User Study on Explanation Completeness and Relevance
  • 4 Experiments
  • 4.1 Experimental Setup
  • 4.2 Evaluating Explanations Generated by State-of-the-Art Methods
  • 4.3 Analyzing Results on CLEVR-X by Question and Answer Types
  • 4.4 Influence of Using Different Numbers of Ground-Truth Explanations
  • 4.5 Qualitative Explanation Generation Results
  • 5 Conclusion
  • References
  • New Developments in Explainable AI
  • A Rate-Distortion Framework for Explaining Black-Box Model Decisions
  • 1 Introduction
  • 2 Related Works
  • 3 Rate-Distortion Explanation Framework
  • 3.1 General Formulation
  • 3.2 Implementation
  • 4 Experiments
  • 4.1 Images
  • 4.2 Audio
  • 4.3 Radio Maps
  • 5 Conclusion
  • References
  • Explaining the Predictions of Unsupervised Learning Models
  • 1 Introduction
  • 2 A Brief Review of Explainable AI
  • 2.1 Approaches to Attribution
  • 2.2 Neuralization-Propagation
  • 3 Kernel Density Estimation
  • 3.1 Explaining Outlierness
  • 3.2 Explaining Inlierness: Direct Approach
  • 3.3 Explaining Inlierness: Random Features Approach
  • 4 K-Means Clustering
  • 4.1 Explaining Cluster Assignments
  • 5 Experiments
  • 5.1 Wholesale Customer Analysis
  • 5.2 Image Analysis
  • 6 Conclusion and Outlook
  • A Attribution on CNN Activations
  • A.1 Attributing Outlierness
  • A.2 Attributing Inlierness
  • A.3 Attributing Cluster Membership
  • References
  • Towards Causal Algorithmic Recourse
  • 1 Introduction
  • 1.1 Motivating Examples
  • 1.2 Summary of Contributions and Structure of This Chapter
  • 2 Preliminaries.
  • 2.1 XAI: Counterfactual Explanations and Algorithmic Recourse
  • 2.2 Causality: Structural Causal Models, Interventions, and Counterfactuals
  • 3 Causal Recourse Formulation
  • 3.1 Limitations of CFE-Based Recourse
  • 3.2 Recourse Through Minimal Interventions
  • 3.3 Negative Result: No Recourse Guarantees for Unknown Structural Equations
  • 4 Recourse Under Imperfect Causal Knowledge
  • 4.1 Probabilistic Individualised Recourse
  • 4.2 Probabilistic Subpopulation-Based Recourse
  • 4.3 Solving the Probabilistic Recourse Optimization Problem
  • 5 Experiments
  • 5.1 Compared Methods
  • 5.2 Metrics
  • 5.3 Synthetic 3-Variable SCMs Under Different Assumptions
  • 5.4 Semi-synthetic 7-Variable SCM for Loan-Approval
  • 6 Discussion
  • 7 Conclusion
  • References
  • Interpreting Generative Adversarial Networks for Interactive Image Generation
  • 1 Introduction
  • 2 Supervised Approach
  • 3 Unsupervised Approach
  • 4 Embedding-Guided Approach
  • 5 Concluding Remarks
  • References
  • XAI and Strategy Extraction via Reward Redistribution
  • 1 Introduction
  • 2 Background
  • 2.1 Explainability Methods
  • 2.2 Reinforcement Learning
  • 2.3 Credit Assignment in Reinforcement Learning
  • 2.4 Methods for Credit Assignment
  • 2.5 Explainability Methods for Credit Assignment
  • 2.6 Credit Assignment via Reward Redistribution
  • 3 Strategy Extraction via Reward Redistribution
  • 3.1 Strategy Extraction with Profile Models
  • 3.2 Explainable Agent Behavior via Strategy Extraction
  • 4 Experiments
  • 4.1 Gridworld
  • 4.2 Minecraft
  • 5 Limitations
  • 6 Conclusion
  • References
  • Interpretable, Verifiable, and Robust Reinforcement Learning via Program Synthesis
  • 1 Introduction
  • 2 Background on Reinforcement Learning
  • 3 Programmatic Policies
  • 3.1 Traditional Interpretable Models
  • 3.2 State Machine Policies
  • 3.3 List Processing Programs.
  • 3.4 Neurosymbolic Policies
  • 4 Synthesizing Programmatic Policies
  • 4.1 Imitation Learning
  • 4.2 Q-Guided Imitation Learning
  • 4.3 Updating the DNN Policy
  • 4.4 Program Synthesis for Supervised Learning
  • 5 Case Studies
  • 5.1 Interpretability
  • 5.2 Verification
  • 5.3 Robustness
  • 6 Conclusions and Future Work
  • References
  • Interpreting and Improving Deep-Learning Models with Reality Checks
  • 1 Interpretability: For What and For Whom?
  • 2 Computing Interpretations for Feature Interactions and Transformations
  • 2.1 Contextual Decomposition (CD) Importance Scores for General DNNs
  • 2.2 Agglomerative Contextual Decomposition (ACD)
  • 2.3 Transformation Importance with Applications to Cosmology (TRIM)
  • 3 Using Attributions to Improve Models
  • 3.1 Penalizing Explanations to Align Neural Networks with Prior Knowledge (CDEP)
  • 3.2 Distilling Adaptive Wavelets from Neural Networks with Interpretations
  • 4 Real-Data Problems Showcasing Interpretations
  • 4.1 Molecular Partner Prediction
  • 4.2 Cosmological Parameter Prediction
  • 4.3 Improving Skin Cancer Classification via CDEP
  • 5 Discussion
  • 5.1 Building/Distilling Accurate and Interpretable Models
  • 5.2 Making Interpretations Useful
  • References
  • Beyond the Visual Analysis of Deep Model Saliency
  • 1 Introduction
  • 2 Saliency-Based XAI in Vision
  • 2.1 White-Box Models
  • 2.2 Black-Box Models
  • 3 XAI for Improved Models: Excitation Dropout
  • 4 XAI for Improved Models: Domain Generalization
  • 5 XAI for Improved Models: Guided Zoom
  • 6 Conclusion
  • References
  • ECQx: Explainability-Driven Quantization for Low-Bit and Sparse DNNs
  • 1 Introduction
  • 2 Related Work
  • 3 Neural Network Quantization
  • 3.1 Entropy-Constrained Quantization
  • 4 Explainability-Driven Quantization
  • 4.1 Layer-Wise Relevance Propagation.
  • 4.2 eXplainability-Driven Entropy-Constrained Quantization
  • 5 Experiments
  • 5.1 Experimental Setup
  • 5.2 ECQx Results
  • 6 Conclusion
  • References
  • A Whale's Tail - Finding the Right Whale in an Uncertain World
  • 1 Introduction
  • 2 Related Work
  • 3 Humpback Whale Data
  • 3.1 Image Data
  • 3.2 Expert Annotations
  • 4 Methods
  • 4.1 Landmark-Based Identification Framework
  • 4.2 Uncertainty and Sensitivity Analysis
  • 5 Experiments and Results
  • 5.1 Experimental Setup
  • 5.2 Uncertainty and Sensitivity Analysis of the Landmarks
  • 5.3 Heatmapping Results and Comparison with Whale Expert Knowledge
  • 5.4 Spatial Uncertainty of Individual Landmarks
  • 6 Conclusion and Outlook
  • References
  • Explainable Artificial Intelligence in Meteorology and Climate Science: Model Fine-Tuning, Calibrating Trust and Learning New Science
  • 1 Introduction
  • 2 XAI Applications
  • 2.1 XAI in Remote Sensing and Weather Forecasting
  • 2.2 XAI in Climate Prediction
  • 2.3 XAI to Extract Forced Climate Change Signals and Anthropogenic Footprint
  • 3 Development of Attribution Benchmarks for Geosciences
  • 3.1 Synthetic Framework
  • 3.2 Assessment of XAI Methods
  • 4 Conclusions
  • References
  • An Interdisciplinary Approach to Explainable AI
  • Varieties of AI Explanations Under the Law. From the GDPR to the AIA, and Beyond
  • 1 Introduction
  • 1.1 Functional Varieties of AI Explanations
  • 1.2 Technical Varieties of AI Explanations
  • 1.3 Roadmap of the Paper
  • 2 Explainable AI Under Current Law
  • 2.1 The GDPR: Rights-Enabling Transparency
  • 2.2 Contract and Tort Law: Technical and Protective Transparency
  • 2.3 Banking Law: More Technical and Protective Transparency
  • 3 Regulatory Proposals at the EU Level: The AIA
  • 3.1 AI with Limited Risk: Decision-Enabling Transparency (Art. 52 AIA)?
  • 3.2 AI with High Risk: Encompassing Transparency (Art. 13 AIA)?.
  • 3.3 Limitations.

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