LEADER 03480nam 22005893u 450 001 9910561298803321 005 20231110230959.0 010 $a3-031-04083-X 035 $a(CKB)5850000000018222 035 $aEBL6954332 035 $a(OCoLC)1311285955 035 $a(AU-PeEL)EBL6954332 035 $a(MiAaPQ)EBC6954332 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/81682 035 $a(PPN)262167603 035 $a(EXLCZ)995850000000018222 100 $a20220617d2022|||| u|| | 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aXxAI - Beyond Explainable AI$b[electronic resource] $eInternational Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Papers 210 $aCham $cSpringer International Publishing AG$d2022 215 $a1 online resource (397 p.) 225 1 $aLecture Notes in Computer Science ;$vv.13200 300 $aDescription based upon print version of record. 311 $a3-031-04082-1 330 $aThis is an open access book. Statistical machine learning (ML) has triggered a renaissance of artificial intelligence (AI). While the most successful ML models, including Deep Neural Networks (DNN), have developed better predictivity, they have become increasingly complex, at the expense of human interpretability (correlation vs. causality). The field of explainable AI (xAI) has emerged with the goal of creating tools and models that are both predictive and interpretable and understandable for humans. Explainable AI is receiving huge interest in the machine learning and AI research communities, across academia, industry, and government, and there is now an excellent opportunity to push towards successful explainable AI applications. This volume will help the research community to accelerate this process, to promote a more systematic use of explainable AI to improve models in diverse applications, and ultimately to better understand how current explainable AI methods need to be improved and what kind of theory of explainable AI is needed. After overviews of current methods and challenges, the editors include chapters that describe new developments in explainable AI. The contributions are from leading researchers in the field, drawn from both academia and industry, and many of the chapters take a clear interdisciplinary approach to problem-solving. The concepts discussed include explainability, causability, and AI interfaces with humans, and the applications include image processing, natural language, law, fairness, and climate science. 410 0$aLecture Notes in Computer Science 606 $aArtificial intelligence$2bicssc 606 $aMachine learning$2bicssc 610 $aComputer Science 610 $aInformatics 610 $aConference Proceedings 610 $aResearch 610 $aApplications 615 7$aArtificial intelligence 615 7$aMachine learning 700 $aHolzinger$b Andreas$0915004 701 $aGoebel$b Randy$0305376 701 $aFong$b Ruth$01239051 701 $aMoon$b Taesup$01239052 701 $aMüller$b Klaus-Robert$01239053 701 $aSamek$b Wojciech$01239054 801 0$bAU-PeEL 801 1$bAU-PeEL 801 2$bAU-PeEL 906 $aBOOK 912 $a9910561298803321 996 $aXxAI - Beyond Explainable AI$92875175 997 $aUNINA