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Transparent Data Mining for Big and Small Data

Overview of attention for book
Cover of 'Transparent Data Mining for Big and Small Data'

Table of Contents

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    Book Overview
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    Chapter 1 The Tyranny of Data? The Bright and Dark Sides of Data-Driven Decision-Making for Social Good
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    Chapter 2 Enabling Accountability of Algorithmic Media: Transparency as a Constructive and Critical Lens
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    Chapter 3 The Princeton Web Transparency and Accountability Project
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    Chapter 4 Algorithmic Transparency via Quantitative Input Influence
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    Chapter 5 Learning Interpretable Classification Rules with Boolean Compressed Sensing
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    Chapter 6 Visualizations of Deep Neural Networks in Computer Vision: A Survey
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    Chapter 7 Beyond the EULA: Improving Consent for Data Mining
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    Chapter 8 Regulating Algorithms’ Regulation? First Ethico-Legal Principles, Problems, and Opportunities of Algorithms
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    Chapter 9 AlgorithmWatch: What Role Can a Watchdog Organization Play in Ensuring Algorithmic Accountability?
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    Chapter 10 Erratum
Attention for Chapter 10: Erratum
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Chapter title
Erratum
Chapter number 10
Book title
Transparent Data Mining for Big and Small Data
Published by
Springer, Cham, January 2017
DOI 10.1007/978-3-319-54024-5_10
Book ISBNs
978-3-31-954023-8, 978-3-31-954024-5
Authors

Tania Cerquitelli, Daniele Quercia, Frank Pasquale

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