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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.
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 book is open access, which means that you have free and unlimited access.
Explainable AI (xAI) aims to create tools and models that are predictive, interpretable, understandable for humans
Topic receiving huge interest in the machine learning and AI research communities
Contributions in this volume are from leading researchers in the field, drawn from both academia and industry
Editorial.- xxAI - Beyond explainable Artificial Intelligence.- Current Methods and Challenges.- Explainable AI Methods - A Brief Overview.- Challenges in Deploying Explainable Machine Learning.- Methods for Machine Learning Models.- CLEVR-X: A Visual Reasoning Dataset for Natural Language Explanations.- New Developments in Explainable AI.- A Rate-Distortion Framework for Explaining Black-box Model Decisions.- Explaining the Predictions of Unsupervised Learning Models.- Towards Causal Algorithmic Recourse.- Interpreting Generative Adversarial Networks for Interactive Image Generation.- XAI and Strategy Extraction via Reward Redistribution.- Interpretable, Verifiable, and Robust Reinforcement Learning via Program Synthesis.- Interpreting and improving deep-learning models with reality checks.- Beyond the Visual Analysis of Deep Model Saliency.- ECQ^2: Quantization for Low-Bit and Sparse DNNs.- A whale's tail - Finding the right whale in an uncertain world.- Explainable Artificial Intelligence in Meteorology and Climate Science: Model fine-tuning, calibrating trust and learning new science.- An Interdisciplinary Approach to Explainable AI.-Varieties of AI Explanations under the Law - From the GDPR to the AIA, and beyond.- Towards Explainability for AI Fairness.- Logic and Pragmatics in AI Explanation.
| Erscheinungsjahr: | 2022 |
|---|---|
| Genre: | Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Seiten: | 408 |
| Reihe: | Lecture Notes in Artificial Intelligence |
| Inhalt: |
x
397 S. 10 s/w Illustr. 114 farbige Illustr. 397 p. 124 illus. 114 illus. in color. |
| ISBN-13: | 9783031040825 |
| ISBN-10: | 3031040821 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | Paperback |
| Einband: | Kartoniert / Broschiert |
| Redaktion: |
Holzinger, Andreas
Goebel, Randy Samek, Wojciech Moon, Taesup Müller, Klaus-Robert Fong, Ruth |
| Herausgeber: | Andreas Holzinger/Randy Goebel/Ruth Fong et al |
| Auflage: | 1st ed. 2022 |
| Hersteller: |
Springer International Publishing
Lecture Notes in Artificial Intelligence |
| Maße: | 235 x 155 x 23 mm |
| Von/Mit: | Andreas Holzinger (u. a.) |
| Erscheinungsdatum: | 17.04.2022 |
| Gewicht: | 0,616 kg |
This book is open access, which means that you have free and unlimited access.
Explainable AI (xAI) aims to create tools and models that are predictive, interpretable, understandable for humans
Topic receiving huge interest in the machine learning and AI research communities
Contributions in this volume are from leading researchers in the field, drawn from both academia and industry
Editorial.- xxAI - Beyond explainable Artificial Intelligence.- Current Methods and Challenges.- Explainable AI Methods - A Brief Overview.- Challenges in Deploying Explainable Machine Learning.- Methods for Machine Learning Models.- CLEVR-X: A Visual Reasoning Dataset for Natural Language Explanations.- New Developments in Explainable AI.- A Rate-Distortion Framework for Explaining Black-box Model Decisions.- Explaining the Predictions of Unsupervised Learning Models.- Towards Causal Algorithmic Recourse.- Interpreting Generative Adversarial Networks for Interactive Image Generation.- XAI and Strategy Extraction via Reward Redistribution.- Interpretable, Verifiable, and Robust Reinforcement Learning via Program Synthesis.- Interpreting and improving deep-learning models with reality checks.- Beyond the Visual Analysis of Deep Model Saliency.- ECQ^2: Quantization for Low-Bit and Sparse DNNs.- A whale's tail - Finding the right whale in an uncertain world.- Explainable Artificial Intelligence in Meteorology and Climate Science: Model fine-tuning, calibrating trust and learning new science.- An Interdisciplinary Approach to Explainable AI.-Varieties of AI Explanations under the Law - From the GDPR to the AIA, and beyond.- Towards Explainability for AI Fairness.- Logic and Pragmatics in AI Explanation.
| Erscheinungsjahr: | 2022 |
|---|---|
| Genre: | Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Seiten: | 408 |
| Reihe: | Lecture Notes in Artificial Intelligence |
| Inhalt: |
x
397 S. 10 s/w Illustr. 114 farbige Illustr. 397 p. 124 illus. 114 illus. in color. |
| ISBN-13: | 9783031040825 |
| ISBN-10: | 3031040821 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | Paperback |
| Einband: | Kartoniert / Broschiert |
| Redaktion: |
Holzinger, Andreas
Goebel, Randy Samek, Wojciech Moon, Taesup Müller, Klaus-Robert Fong, Ruth |
| Herausgeber: | Andreas Holzinger/Randy Goebel/Ruth Fong et al |
| Auflage: | 1st ed. 2022 |
| Hersteller: |
Springer International Publishing
Lecture Notes in Artificial Intelligence |
| Maße: | 235 x 155 x 23 mm |
| Von/Mit: | Andreas Holzinger (u. a.) |
| Erscheinungsdatum: | 17.04.2022 |
| Gewicht: | 0,616 kg |
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