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008			220416s2022 sz | s |||| 0|eng d
020			_a9783031040832
024	7		_a10.1007/978-3-031-04083-2 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ334 _b2022 EB
245	0	0	_axxAI - Beyond Explainable AI : _bInternational Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Papers _cedited by Andreas Holzinger, Randy Goebel, Ruth Fong, Taesup Moon, Klaus-Robert Müller, Wojciech Samek
250			_aFirst edition 2022
264		1	_aCham _bSpringer International Publising _c2022
300			_a1 recurso en línea (X, 397 páginas) _b124 ilustraciones, 114 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aLecture Notes in Artificial Intelligence _v13200
505	0		_aEditorial -- 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.
506	0		_aOpen Access
520			_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.
988			_aSpringer_Computer_2022
650		7	_2embne _aInteligencia artificial _vCongresos y asambleas _9413115
650		7	_2embne _9166090 _aAprendizaje automático _vCongresos y asambleas
700	1		_aHolzinger, Andreas _eeditor literario _0(orcid)0000-0002-6786-5194 _1https://orcid.org/0000-0002-6786-5194 _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aGoebel, Randy _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aFong, Ruth _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aMoon, Taesup _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aMüller, Klaus-Robert _eeditor literario _0(orcid)0000-0002-3861-7685 _1https://orcid.org/0000-0002-3861-7685 _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aSamek, Wojciech _eeditor literario _0(orcid)0000-0002-6283-3265 _1https://orcid.org/0000-0002-6283-3265 _4edt _4http://id.loc.gov/vocabulary/relators/edt
776	0	8	_iPrinted edition: _z9783031040825
776	0	8	_iPrinted edition: _z9783031040849
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-04083-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b11/2022 _dz _eIG _zSI