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Call for Book Chapter Contributions
We are co-editing a  Springer book titled “Towards Transparent Data Mining
for Big and Small Data”.  http://dbdmg.polito.it/glass-boxDM/

We are looking for contributions by experts in the emerging field of
"transparent data algorithms". In a first round, we selected contributors
for a variety of chapters. To be as inclusive as possible, we are opening a
second round in which anyone can submit a relevant abstract. The submitted
abstracts will be peer-reviewed.

The call for contributions will be open for only two weeks. The abstract
submission deadline is May 20th, 2016. A one-page chapter proposal should
be sent in PDF format using the online submission system
https://easychair.org/conferences/?conf=glassboxdm2016

For further information, please contact  [log in to unmask]

- Tania Cerquitelli, Daniele Quercia, and Frank Pasquale

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Details of the book

Important deadlines
Abstract submission: May 20th, 2016
Book Chapter submission: September 9th, 2016
Review Notification: October 14th, 2016
Camera ready: November 30th , 2016

Title: “Towards glass-box data mining for Big and Small Data”

Subject matter:
This book will focus on new emerging data analytics solutions that offer
a greater level of transparency than existing solutions. The vast
majority of existing algorithms are opaque – that is, the internal
algorithmic mechanics are not transparent in that they produce output
without making it clear how they have done so. As algorithms
increasingly support different aspects of our life, a greater level of
transparency is badly needed, not least because discrimination and
biases have to be avoided.

Exposing the algorithms software is a challenging task. Although we
barely notice them, they are behind a large part of the information we
use every day, so rendering algorithms more transparent should improve
their usability in various application domains. Thus, transparent
solutions are needed to produce more credible and reliable information
and services, playing a key role in proactive user engagement by making
the results of the data analytical process and its models widely accessible.
The book will include chapters that discuss/address transparent data
algorithms for Big and Small Data from different research directions,
such as data mining, machine learning, digital ethics, and applied
statistics. Book chapters will be written by experts in the emerging
field on transparent data algorithms and they will be peer reviewed.

Topics of interest include:
| Accurate and transparent predictive models
| Exploratory and user-controlled data mining models
| Adaptive Glass-box algorithms
| Explaining Glass-box algorithms
| Educating to Glass-box algorithms
| Ethical Issues of Data Collection, Storage, and Exchange
| Algorithmic Accountability
| Restrictions on Data and Algorithmic Inferences
| Applications in Social Sciences
| Political Economy and Social Theory for Algorithmic Processing

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