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020 _a9783319540245
_9978-3-319-54024-5
024 7 _a10.1007/978-3-319-54024-5
_2doi
050 4 _aQA76.9.D343
072 7 _aUNF
_2bicssc
072 7 _aUYQE
_2bicssc
072 7 _aCOM021030
_2bisacsh
072 7 _aUNF
_2thema
072 7 _aUYQE
_2thema
082 0 4 _a006.312
_223
245 1 0 _aTransparent Data Mining for Big and Small Data
_h[electronic resource] /
_cedited by Tania Cerquitelli, Daniele Quercia, Frank Pasquale.
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aXV, 215 p. 23 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Big Data,
_x2197-6511 ;
_v32
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 _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.
650 0 _aData mining.
_93907
650 0 _aInformation technology—Law and legislation.
_948877
650 0 _aMass media—Law and legislation.
_948878
650 0 _aAlgorithms.
_93390
650 0 _aDynamics.
_958827
650 0 _aNonlinear theories.
_93339
650 0 _aComputer simulation.
_95106
650 0 _aQuantitative research.
_94633
650 1 4 _aData Mining and Knowledge Discovery.
_958828
650 2 4 _aIT Law, Media Law, Intellectual Property.
_948879
650 2 4 _aAlgorithms.
_93390
650 2 4 _aApplied Dynamical Systems.
_932005
650 2 4 _aComputer Modelling.
_958829
650 2 4 _aData Analysis and Big Data.
_958830
700 1 _aCerquitelli, Tania.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_958831
700 1 _aQuercia, Daniele.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_958832
700 1 _aPasquale, Frank.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_958833
710 2 _aSpringerLink (Online service)
_958834
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319540238
776 0 8 _iPrinted edition:
_z9783319540252
776 0 8 _iPrinted edition:
_z9783319852997
830 0 _aStudies in Big Data,
_x2197-6511 ;
_v32
_958835
856 4 0 _uhttps://doi.org/10.1007/978-3-319-54024-5
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c80224
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