About this book
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.
Transparent Predictive Models Glass-box Algorithms Black-box Algorithms Transparent vs Opaque Algorithms Automated Decision Making Big Data Paradigm Shift
Editors and affiliations
- DOI https://doi.org/10.1007/978-3-319-54024-5
- Copyright Information Springer International Publishing AG 2017
- Publisher Name Springer, Cham
- eBook Packages Engineering Engineering (R0)
- Print ISBN 978-3-319-54023-8
- Online ISBN 978-3-319-54024-5
- Series Print ISSN 2197-6503
- Series Online ISSN 2197-6511
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