Transparent Data Mining for Big and Small Data / edited by Tania Cerquitelli, Daniele Quercia, Frank Pasquale.
2017
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Title
Transparent Data Mining for Big and Small Data / edited by Tania Cerquitelli, Daniele Quercia, Frank Pasquale.
Added Corporate Author
Edition
1st ed. 2017.
Imprint
Cham : Springer International Publishing : Imprint: Springer, 2017.
Description
XV, 215 p. 23 illus. in color. online resource.
Series
Studies in big data. 2197-6503 ; 32.
Formatted Contents Note
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?
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?
Summary
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.
Location
www
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Alternate Title
SpringerLink electronic monographs.
Language
English
ISBN
9783319540245
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