Open call
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Call for Book Chapter Contributions
We are co-editing a Springer book titled “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 tania DOT cerquitelli AT polito DOT it
- 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 transparent 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 transparent algorithms
| Explaining transparent algorithms
| Educating to transparent 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
