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Studies in Big Data

Transparent Data Mining for Big and Small Data

Editors: Cerquitelli, Tania, Quercia, Daniele, Pasquale, Frank (Eds.)

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  • Describes the negative effects of opaque "black-box" algorithms in technical detail
  • Offers solutions for the implementation of transparent algorithms
  • Discusses specific state-of-the-art transparent algorithms as well as new applications made possible by transparent algorithms
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  • ISBN 978-3-319-54024-5
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Hardcover 124,79 €
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  • ISBN 978-3-319-54023-8
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Softcover 124,79 €
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About this book

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.

Table of contents (10 chapters)

Table of contents (10 chapters)
  • The Tyranny of Data? The Bright and Dark Sides of Data-Driven Decision-Making for Social Good

    Pages 3-24

    Lepri, Bruno (et al.)

  • Enabling Accountability of Algorithmic Media: Transparency as a Constructive and Critical Lens

    Pages 25-43

    Diakopoulos, Nicholas

  • The Princeton Web Transparency and Accountability Project

    Pages 45-67

    Narayanan, Arvind (et al.)

  • Algorithmic Transparency via Quantitative Input Influence

    Pages 71-94

    Datta, Anupam (et al.)

  • Learning Interpretable Classification Rules with Boolean Compressed Sensing

    Pages 95-121

    Malioutov, Dmitry M. (et al.)

Buy this book

eBook 96,29 €
price for Spain (gross)
  • ISBN 978-3-319-54024-5
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 124,79 €
price for Spain (gross)
  • ISBN 978-3-319-54023-8
  • Free shipping for individuals worldwide
  • Immediate ebook access, if available*, with your print order
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
Softcover 124,79 €
price for Spain (gross)
  • ISBN 978-3-319-85299-7
  • Free shipping for individuals worldwide
  • Immediate ebook access, if available*, with your print order
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
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Bibliographic Information

Bibliographic Information
Book Title
Transparent Data Mining for Big and Small Data
Editors
  • Tania Cerquitelli
  • Daniele Quercia
  • Frank Pasquale
Series Title
Studies in Big Data
Series Volume
32
Copyright
2017
Publisher
Springer International Publishing
Copyright Holder
Springer International Publishing AG, part of Springer Nature
eBook ISBN
978-3-319-54024-5
DOI
10.1007/978-3-319-54024-5
Hardcover ISBN
978-3-319-54023-8
Softcover ISBN
978-3-319-85299-7
Series ISSN
2197-6503
Edition Number
1
Number of Pages
XV, 215
Number of Illustrations
23 illustrations in colour
Topics

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