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
Corporate Author: SpringerLink (Online service)
Other Authors: Cerquitelli, Tania (Editor), Quercia, Daniele (Editor), Pasquale, Frank (Editor)
Format: e-Book
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2017.
Edition:1st ed. 2017.
Series:Studies in Big Data, 32
Subjects:
Data mining.
Information technology > Law and legislation.
Mass media > Law and legislation.
Algorithms.
Dynamics.
Nonlinear theories.
Computer simulation.
Quantitative research.
Data Mining and Knowledge Discovery.
IT Law, Media Law, Intellectual Property.
Applied Dynamical Systems.
Computer Modelling.
Data Analysis and Big Data.
Online Access:Full-text access
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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 AlgorithmicAccountability?

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