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Transparent Data Mining for Big and Small Data

  • Tania Cerquitelli
  • Daniele Quercia
  • Frank Pasquale
Book

Part of the Studies in Big Data book series (SBD, volume 32)

Table of contents

  1. Front Matter
    Pages i-xv
  2. Transparent Mining

    1. Front Matter
      Pages 1-1
    2. Bruno Lepri, Jacopo Staiano, David Sangokoya, Emmanuel Letouzé, Nuria Oliver
      Pages 3-24
    3. Arvind Narayanan, Dillon Reisman
      Pages 45-67
  3. Algorithmic Solutions

    1. Front Matter
      Pages 69-69
    2. Anupam Datta, Shayak Sen, Yair Zick
      Pages 71-94
    3. Dmitry M. Malioutov, Kush R. Varshney, Amin Emad, Sanjeeb Dash
      Pages 95-121
    4. Christin Seifert, Aisha Aamir, Aparna Balagopalan, Dhruv Jain, Abhinav Sharma, Sebastian Grottel et al.
      Pages 123-144
  4. Regulatory Solutions

  5. Tania Cerquitelli, Daniele Quercia, Frank Pasquale
    Pages E1-E1

About this book

Introduction

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.

Keywords

Transparent Predictive Models Glass-box Algorithms Black-box Algorithms Transparent vs Opaque Algorithms Automated Decision Making Big Data Paradigm Shift

Editors and affiliations

  • Tania Cerquitelli
    • 1
  • Daniele Quercia
    • 2
  • Frank Pasquale
    • 3
  1. 1.Department of Control and Computer EngineeringPolitecnico di TorinoTorinoItaly
  2. 2.Bell LaboratoriesCambridgeUnited Kingdom
  3. 3.Carey School of LawUniversity of MarylandBaltimoreUSA

Bibliographic information

  • 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
  • Buy this book on publisher's site
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