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xxAI -- beyond explainable AI = Inte...

xxAI (Workshop) ((2020 :)

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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 /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	xxAI -- beyond explainable AI/ edited by Andreas Holzinger ... [et al.].
其他題名:	International Workshop, held in conjunction with ICML 2020, July 18, 2020, Vienna, Austria : revised and extended papers /
其他作者:	Holzinger, Andreas.
團體作者:	xxAI (Workshop)
出版者:	Cham :Springer International Publishing : : 2022.,
面頁冊數:	x, 397 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Artificial intelligence - Congresses. -
電子資源:	https://doi.org/10.1007/978-3-031-04083-2
ISBN:	9783031040832

xxAI -- beyond explainable AI = International Workshop, held in conjunction with ICML 2020, July 18, 2020, Vienna, Austria : revised and extended papers /
xxAI -- beyond explainable AIInternational Workshop, held in conjunction with ICML 2020, July 18, 2020, Vienna, Austria : revised and extended papers /[electronic resource] :edited by Andreas Holzinger ... [et al.]. - Cham :Springer International Publishing :2022. - x, 397 p. :ill. (some col.), digital ;24 cm. - Lecture notes in computer science,132000302-9743 ;. - Lecture notes in computer science ;13200..

Open access.

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.

ISBN: 9783031040832

Standard No.: 10.1007/978-3-031-04083-2doiSubjects--Topical Terms:

606815
Artificial intelligence
--Congresses.

LC Class. No.: Q334 / .X83 2020

Dewey Class. No.: 006.3

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