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000782080 24500 $$aTransparent data mining for big and small data /$$cTania Cerquitelli, Daniele Quercia, Frank Pasquale, editors.
000782080 264_1 $$aCham :$$bSpringer International Publishing :$$bImprint: Springer,$$c2017.
000782080 300__ $$a1 online resource (xv, 215 pages) :$$billustrations.
000782080 336__ $$atext$$btxt$$2rdacontent
000782080 337__ $$acomputer$$bc$$2rdamedia
000782080 338__ $$aonline resource$$bcr$$2rdacarrier
000782080 4901_ $$aStudies in big data ;$$vvolume 11
000782080 5050_ $$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?
000782080 506__ $$aAccess limited to authorized users.
000782080 588__ $$aDescription based on print version record.
000782080 650_0 $$aData mining.
000782080 7001_ $$aCerquitelli, Tania.
000782080 7001_ $$aQuercia, Daniele.
000782080 7001_ $$aPasquale, Frank.
000782080 77608 $$iPrint version:$$z3319540238$$z9783319540238$$w(OCoLC)969884791
000782080 830_0 $$aStudies in big data ;$$vv. 11.
000782080 85280 $$bebk$$hSpringerLink
000782080 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-54024-5$$zOnline Access$$91397441.1
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