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Transparent data mining for big and ...

Cerquitelli, Tania.

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Transparent data mining for big and small data

Record Type:	Electronic resources : Monograph/item
Title/Author:	Transparent data mining for big and small data/ edited by Tania Cerquitelli, Daniele Quercia, Frank Pasquale.
other author:	Cerquitelli, Tania.
Published:	Cham :Springer International Publishing : : 2017.,
Description:	xv, 215 p. :ill., digital ;24 cm.
[NT 15003449]:	Part 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?
Contained By:	Springer eBooks
Subject:	Data mining. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-54024-5
ISBN:	9783319540245

Transparent data mining for big and small data
Transparent data mining for big and small data[electronic resource] /edited by Tania Cerquitelli, Daniele Quercia, Frank Pasquale. - Cham :Springer International Publishing :2017. - xv, 215 p. :ill., digital ;24 cm. - Studies in big data,v.322197-6503 ;. - Studies in big data ;v.32..

Part 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?

This 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.

ISBN: 9783319540245

Standard No.: 10.1007/978-3-319-54024-5doiSubjects--Topical Terms:

562972
Data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Transparent data mining for big and small data
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245 0 0 $a Transparent data mining for big and small data $h [electronic resource] / $c edited by Tania Cerquitelli, Daniele Quercia, Frank Pasquale.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2017.
300 $a xv, 215 p. : $b ill., digital ; $c 24 cm.
490 1 $a Studies in big data, $x 2197-6503 ; $v v.32
505 0 $a Part 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 $a This 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 $a Data mining. $3 562972
650 1 4 $a Computer Science. $3 626642
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a International IT and Media Law, Intellectual Property Law. $3 1568595
650 2 4 $a Algorithm Analysis and Problem Complexity. $3 891007
650 2 4 $a Complexity. $3 893807
650 2 4 $a Simulation and Modeling. $3 890873
650 2 4 $a Big Data/Analytics. $3 2186785
700 1 $a Cerquitelli, Tania. $3 3242208
700 1 $a Quercia, Daniele. $3 3242209
700 1 $a Pasquale, Frank. $3 3242210
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Studies in big data ; $v v.32. $3 3242211
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-54024-5
950 $a Engineering (Springer-11647)