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008			170509s2017 sz a o 000 0 eng d
020			_a3319540246
020			_a9783319540245
020			_z3319540238
020			_z9783319540238
040			_aAZU _cAZU _dOCLCO _dUPM _dYDX _dN$T _dEBLCP _dGW5XE _dOCLCF _dUAB _dESU _dNJR _dIOG _dCOO _dMERER _dOCLCQ _dOCL _dOH1 _dOCLCQ _dES-MaUEC _bspa
050		4	_aQA76.9.D343 _bT736 2017 EB
245	0	0	_aTransparent Data Mining for Big and Small Data _cedited by Tania Cerquitelli, Daniele Quercia, Frank Pasquale.
264		1	_aCham _bSpringer International Publishing _c2017.
300			_a1 recurso en línea (XV, 215 páginas) _bilustraciones (algunas a color)
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF
490	0		_aStudies in Big Data _x2197-6503 _v32
500			_aSpringerLink
505	0		_aPart I: Transparent Mining -- Chapter 1: The Tyranny of Data? The Bright and Dark Sides of Data-Driven Decision-Making for Social Good -- Chapter 2: Enabling Accountability of Algorithmic Media: Transparency as a Constructive and Critical Lens -- Chapter 3: The Princeton Web Transparency and Accountability Project -- Part II: Algorithmic solutions -- Chapter 4: Algorithmic Transparency via Quantitative Input Influence -- Chapter 5 -- Learning Interpretable Classification Rules with Boolean Compressed Sensing -- Chapter 6: Visualizations of Deep Neural Networks in Computer Vision: A Survey -- Part III: Regulatory solutions -- Chapter 7: Beyond the EULA: Improving Consent for Data Mining -- Chapter 8: Regulating Algorithms Regulation? First Ethico-legal Principles, Problems and Opportunities of Algorithms -- Chapter 9: Algorithm Watch: What Role Can a Watchdog Organization Play in Ensuring Algorithmic Accountability?
520	3		_aThis book focuses on new and emerging data mining solutions that offer a greater level of transparency than existing solutions. Transparent data mining solutions with desirable properties (e.g. effective, fully automatic, scalable) are covered in the book. Experimental findings of transparent solutions are tailored to different domain experts, and experimental metrics for evaluating algorithmic transparency are presented. The book also discusses societal effects of black box vs. transparent approaches to data mining, as well as real-world use cases for these approaches. As algorithms increasingly support different aspects of modern life, a greater level of transparency is sorely needed, not least because discrimination and biases have to be avoided. With contributions from domain experts, this book provides an overview of an emerging area of data mining that has profound societal consequences, and provides the technical background to for readers to contribute to the field or to put existing approaches to practical use.
988			_aEBOOK, asignarmaterias, EBSPRINGER_2017D
650		7	_aAlgoritmos _2embne _0(OCoLC)fst00805020 _0 _9141162
700	1		_aCerquitelli, Tania _eeditor literario _999962
700	1		_aPasquale, Frank, _eeditor literario
700	1		_aQuercia, Daniele, _eeditor literario
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://link.springer.com/10.1007/978-3-319-54024-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cM1S
998			_b02/2018 _dz _e- _zSI