Overview
- 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
- Includes supplementary material: sn.pub/extras
Part of the book series: Studies in Big Data (SBD, volume 32)
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Table of contents (10 chapters)
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Front Matter
Keywords
About this book
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.
Editors and Affiliations
Bibliographic Information
Book Title: Transparent Data Mining for Big and Small Data
Editors: Tania Cerquitelli, Daniele Quercia, Frank Pasquale
Series Title: Studies in Big Data
DOI: https://doi.org/10.1007/978-3-319-54024-5
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing AG, part of Springer Nature 2017
Hardcover ISBN: 978-3-319-54023-8Published: 15 May 2017
Softcover ISBN: 978-3-319-85299-7Published: 28 July 2018
eBook ISBN: 978-3-319-54024-5Published: 09 May 2017
Series ISSN: 2197-6503
Series E-ISSN: 2197-6511
Edition Number: 1
Number of Pages: XV, 215
Number of Illustrations: 23 illustrations in colour
Topics: Data Mining and Knowledge Discovery, IT Law, Media Law, Intellectual Property, Algorithm Analysis and Problem Complexity, Complexity, Simulation and Modeling, Big Data/Analytics