xxAI - Beyond Explainable AI International Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Papers
This is an open access book. Statistical machine learning (ML) has triggered a renaissance of artificial intelligence (AI). While the most successful ML models, including Deep Neural Networks (DNN), have developed better predictivity, they have become increasingly complex, at the expense of human in...
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| Médium: | E-kniha Kniha |
| Jazyk: | English |
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Springer Nature
2022
Springer Springer International Publishing AG |
| Vydanie: | 1 |
| Edícia: | Lecture Notes in Computer Science; Lecture Notes in Artificial Intelligence |
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| ISBN: | 9783031040832, 303104083X, 9783031040825, 3031040821 |
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| Abstract | This is an open access book. Statistical machine learning (ML) has triggered a renaissance of artificial intelligence (AI). While the most successful ML models, including Deep Neural Networks (DNN), have developed better predictivity, they have become increasingly complex, at the expense of human interpretability (correlation vs. causality). The field of explainable AI (xAI) has emerged with the goal of creating tools and models that are both predictive and interpretable and understandable for humans. Explainable AI is receiving huge interest in the machine learning and AI research communities, across academia, industry, and government, and there is now an excellent opportunity to push towards successful explainable AI applications. This volume will help the research community to accelerate this process, to promote a more systematic use of explainable AI to improve models in diverse applications, and ultimately to better understand how current explainable AI methods need to be improved and what kind of theory of explainable AI is needed. After overviews of current methods and challenges, the editors include chapters that describe new developments in explainable AI. The contributions are from leading researchers in the field, drawn from both academia and industry, and many of the chapters take a clear interdisciplinary approach to problem-solving. The concepts discussed include explainability, causability, and AI interfaces with humans, and the applications include image processing, natural language, law, fairness, and climate science. |
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| AbstractList | This is an open access book. Statistical machine learning (ML) has triggered a renaissance of artificial intelligence (AI). While the most successful ML models, including Deep Neural Networks (DNN), have developed better predictivity, they have become increasingly complex, at the expense of human interpretability (correlation vs. causality). The field of explainable AI (xAI) has emerged with the goal of creating tools and models that are both predictive and interpretable and understandable for humans. Explainable AI is receiving huge interest in the machine learning and AI research communities, across academia, industry, and government, and there is now an excellent opportunity to push towards successful explainable AI applications. This volume will help the research community to accelerate this process, to promote a more systematic use of explainable AI to improve models in diverse applications, and ultimately to better understand how current explainable AI methods need to be improved and what kind of theory of explainable AI is needed. After overviews of current methods and challenges, the editors include chapters that describe new developments in explainable AI. The contributions are from leading researchers in the field, drawn from both academia and industry, and many of the chapters take a clear interdisciplinary approach to problem-solving. The concepts discussed include explainability, causability, and AI interfaces with humans, and the applications include image processing, natural language, law, fairness, and climate science. This is an open access book.Statistical machine learning (ML) has triggered a renaissance of artificial intelligence (AI).While the most successful ML models, including Deep Neural Networks (DNN), have developed better predictivity, they have become increasingly complex, at the expense of human interpretability (correlation vs.causality). |
| Author | Holzinger, Andreas International Conference on Machine Learning xxAI - beyond explainable AI (Workshop) (2020 : Vienna, Austria) |
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| Editor | Holzinger, Andreas Moon, Taesup Samek, Wojciech Müller, Klaus-Robert Goebel, Randy Fong, Ruth |
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| Notes | Includes bibliographical references and index |
| OCLC | OCN: 1311285955 1311285955 |
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| Snippet | This is an open access book. Statistical machine learning (ML) has triggered a renaissance of artificial intelligence (AI). While the most successful ML... This is an open access book.Statistical machine learning (ML) has triggered a renaissance of artificial intelligence (AI).While the most successful ML models,... This is an open access book.Statistical machine learning (ML) has triggered a renaissance of artificial intelligence (AI). While the most successful ML models,... |
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| SubjectTerms | Applications Artificial intelligence Artificial intelligence -- Congresses Computer Science Human-computer interaction Human-computer interaction -- Congresses Informatics Machine learning Machine learning -- Congresses Special computer methods |
| Subtitle | International Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Papers |
| TableOfContents | 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)? 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 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 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 3.3 Limitations 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 |
| Title | xxAI - Beyond Explainable AI |
| URI | http://digital.casalini.it/9783031040832 https://cir.nii.ac.jp/crid/1130858596794727552 https://directory.doabooks.org/handle/20.500.12854/81682 https://library.oapen.org/handle/20.500.12657/54443 https://ebookcentral.proquest.com/lib/[SITE_ID]/detail.action?docID=6954332 https://www.vlebooks.com/vleweb/product/openreader?id=none&isbn=9783031040832 |
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