Transparent data mining for big and small data

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 soluti...

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Bibliographic Details
Other Authors Cerquitelli, Tania (Editor), Quercia, Daniele (Editor), Pasquale, Frank (Editor)
Format Electronic eBook
LanguageEnglish
Published Cham : Springer, 2017.
SeriesStudies in big data ; 32.
Subjects
Data mining.
elektronické knihy
electronic books
Online AccessFull text
ISBN9783319540245
9783319540238
ISSN2197-6503 ;
Physical Description1 online resource (XV, 215 pages) : 23 illustrations in color

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Table of Contents:
  • 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?

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