Table of content

Springer Logo
Transparent data mining 
for Big and Small Data

The book will consist of about 10 chapters written by an international array of experts working at the intersection of data science, data mining, and social science. We are organizing the book in two parts providing both the technical and political background to either further the literature in the field or put existing approaches to practical use.  Specifically, one contribution for each of the following topics will be included

PART I - Concepts, methodologies, and algorithms for transparent data mining

1. Accurate and transparent predictive models

2. Exploratory and user-controlled data mining models

3. Adaptive transparent algorithms

4. Explaining transparent algorithms

5. Educating to transparent algorithms

PART II - Social aspects of transparent data mining

6. Ethical Issues of Data Collection, Storage, and Exchange

7. Algorithmic Accountability

8. Restrictions on Data and Algorithmic Inferences

9. Applications in Social Sciences

10. Political Economy and Social Theory for Algorithmic Processing