Holzinger Andreas, Goebel Randy, Fong Ruth xxAI - Beyond Explainable AI: International Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Paper 9783031040825

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Автор: Holzinger Andreas, Goebel Randy, Fong Ruth
Название: xxAI - Beyond Explainable AI: International Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Paper
ISBN: 9783031040825
Издательство: Springer
Классификация:

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ISBN-10: 3031040821
Обложка/Формат: Paperback
Страницы: 408
Вес: 0.57 кг.
Дата издания: 17.04.2022
Серия: Lecture notes in artificial intelligence
Язык: English
Издание: 1st ed. 2022
Иллюстрации: 114 illustrations, color; 10 illustrations, black and white; x, 397 p. 124 illus., 114 illus. in color.; 114 illustrations, color; 10 illustrations, b
Размер: 23.39 x 15.60 x 2.13 cm
Читательская аудитория: Professional & vocational
Подзаголовок: International workshop, held in conjunction with icml 2020, july 18, 2020, vienna, austria, revised and extended papers
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: 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.
Дополнительное описание: 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 Rea

xxAI - Beyond Explainable AI: International Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Paper, Holzinger Andreas, Goebel Randy, Fong Ruth