Transparent Data Mining for Big and Small Data

✍ Scribed by Tania Cerquitelli, Daniele Quercia, Frank Pasquale (eds.)

Publisher: Springer International Publishing
Year: 2017
Tongue: English
Leaves: 223
Series: Studies in Big Data 32
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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

Front Matter....Pages i-xv
Front Matter....Pages 1-1
The Tyranny of Data? The Bright and Dark Sides of Data-Driven Decision-Making for Social Good....Pages 3-24
Enabling Accountability of Algorithmic Media: Transparency as a Constructive and Critical Lens....Pages 25-43
The Princeton Web Transparency and Accountability Project....Pages 45-67
Front Matter....Pages 69-69
Algorithmic Transparency via Quantitative Input Influence....Pages 71-94
Learning Interpretable Classification Rules with Boolean Compressed Sensing....Pages 95-121
Visualizations of Deep Neural Networks in Computer Vision: A Survey....Pages 123-144
Front Matter....Pages 145-145
Beyond the EULA: Improving Consent for Data Mining....Pages 147-167
Regulating Algorithms’ Regulation? First Ethico-Legal Principles, Problems, and Opportunities of Algorithms....Pages 169-206
AlgorithmWatch: What Role Can a Watchdog Organization Play in Ensuring Algorithmic Accountability?....Pages 207-215

✦ Subjects

Data Mining and Knowledge Discovery;International IT and Media Law, Intellectual Property Law;Algorithm Analysis and Problem Complexity;Complexity;Simulation and Modeling;Big Data/Analytics

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