Transparent Data Mining for Big and Small Data
Editors: Cerquitelli, Tania, Quercia, Daniele, Pasquale, Frank (Eds.)
Free Preview- Describes the negative effects of opaque "black-box" algorithms in technical detail
- Offers solutions for the implementation of transparent algorithms
- Discusses specific state-of-the-art transparent algorithms as well as new applications made possible by transparent algorithms
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- About this book
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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 (10 chapters)
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The Tyranny of Data? The Bright and Dark Sides of Data-Driven Decision-Making for Social Good
Pages 3-24
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Enabling Accountability of Algorithmic Media: Transparency as a Constructive and Critical Lens
Pages 25-43
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The Princeton Web Transparency and Accountability Project
Pages 45-67
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Algorithmic Transparency via Quantitative Input Influence
Pages 71-94
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Learning Interpretable Classification Rules with Boolean Compressed Sensing
Pages 95-121
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Table of contents (10 chapters)
- Download Preface 1 PDF (39.6 KB)
- Download Sample pages 1 PDF (1.6 MB)
- Download Table of contents PDF (34.2 KB)
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Bibliographic Information
- Bibliographic Information
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- Book Title
- Transparent Data Mining for Big and Small Data
- Editors
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- Tania Cerquitelli
- Daniele Quercia
- Frank Pasquale
- Series Title
- Studies in Big Data
- Series Volume
- 32
- Copyright
- 2017
- Publisher
- Springer International Publishing
- Copyright Holder
- Springer International Publishing AG, part of Springer Nature
- eBook ISBN
- 978-3-319-54024-5
- DOI
- 10.1007/978-3-319-54024-5
- Hardcover ISBN
- 978-3-319-54023-8
- Softcover ISBN
- 978-3-319-85299-7
- Series ISSN
- 2197-6503
- Edition Number
- 1
- Number of Pages
- XV, 215
- Number of Illustrations
- 23 illustrations in colour
- Topics
