[ecrea] cfp Preconference Digital inequalities and discrimination in the big data era

*DIGITAL INEQUALITIES AND DISCRIMINATION IN THE BIG DATA ERA*


/Preconference of the International Communication Association '17/


May 25, 2017, San Diego Hilton Bayfront, San Diego, California (USA)


Co-sponsored by the Pacific ICTD Collaborative, the School of 
Communications (University of Hawaii at Manoa), and the Institute for 
Information Policy (Penn State University)


Abstracts due: February 10, 2017


*CALL FOR PAPERS*


A growing number of ordinary objects are being redesigned to include 
digital sensors, computing power, and communication capabilities – and 
new objects, and processes, are becoming part of the Internet. This 
emerging Internet of Things (IoT) ecosystem – networks of physical 
objects embedded with the ability to sense, and sometimes act upon, 
their environment, as well as related communication, applications, and 
data analysis, enables data to be collected from billions of everyday 
objects. The emerging datasphere made possible by these developments 
offers immense potential to serve the public good by fostering 
government transparency, energy conservation, participatory governance, 
and substantial advances in medical research and care. On the other 
hand, a growing body of research addresses emerging privacy and civil 
liberties concerns related to big data, including unjust discrimination 
and unequal access to data and the tools needed to make use of it.


For example, big data analytics may reveal patterns that were previously 
not detectable. Data about a variety of daily tasks that seem trivial is 
increasingly being federated and used to reveal associations or 
behaviors, and these analyses and the decisions made based on them pose 
potential harms to individuals or groups. Many transactions that seemed 
innocuous can now be used to discriminate – one’s movement throughout 
the day, items purchased at the store, television programs watched, 
“friends” added or looked at on social networks, or individuals 
communicated with or who were in close proximity to the subject at 
various times, can all be used to make judgements that affect an 
individual and his or her life chances. With the advent of artificial 
intelligence and machine learning, we are increasingly moving to a world 
where many decisions around us are shaped by these calculations rather 
than traditional human judgement. For example, sensitive personal 
information or behaviors (e.g., political or health-related) may be used 
to discriminate when individuals seek housing, immigration eligibility, 
medical care, education, bank loans or other financial services, 
insurance, or employment. At the same time, individuals, groups, or 
regions may also be disadvantaged due to a lack of access to data (or 
related skills and tools) to make use of big data in ways that benefit 
their lives and communities.


This preconference session seeks to advance understanding of digital 
inequalities and discrimination related to big data and big data 
analytics. *Papers between 5,000-8,000 words and position papers between 
1,000-2,000 words are welcomed. *


*TOPICS OF INTEREST*


We welcome scholarly and applied research on, but not limited to, the 
following:


• Social, economic, and ethical implications of big data analytics in a 
variety of contexts (e.g., access to housing, immigration, medical care, 
education, bank loans or other financial services, insurance, or 
employment).

• Perspectives on big data from scholars from emerging economies or 
traditionally marginalized groups.

• Predictive analytics, algorithmic discrimination, and 
artificial-intelligence-based decision making.

• Digital inequalities, such as unequal access to big data sets, skills, 
or tools.

• Emerging data literacies.

• Use of big data to counter social and economic inequality (e.g., 
promoting civil rights and social justice).

• Disclosure of algorithms, algorithmic transparency, and the public good.

• Big data, security and encryption (potential for hacking, theft, 
third-party abuse).

• Government and corporate surveillance.

• Big data brokers and sale of personal data (is privacy a commodity or 
a right?)

• International norms and standards for big data.

• Policy/legal analysis related to big data and the preconference theme 
(e.g., standards of liability for injury and defective work products 
(algorithms/burden of proof), the challenge of Notice and Consent, 
liability for bad or false or slanted or insufficient data collection, 
government regimes for supervision of big data policies).

• Consumer bill of rights for big data.

• Big data and anonymity, re-identification of anonymous data.

• Big data vs. privacy as an essential condition for safeguarding free 
speech, intellectual property (i.e., how IP laws impact big data), or 
Constitutional rights of freedom of assembly and association.


Papers may include empirical research as well as policy analyses, new 
methodological approaches, or position papers addressing the 
preconference theme.  Submissions by graduate students working in this 
area are welcomed.


/The costs of the workshop are heavily subsidized by the participating 
Institutes, to keep fees for participants at a nominal level./


*IMPORTANT DATES*


*Abstracts due*: February 10, 2017

*Notifications to submitters*: February 27, 2017

*Full papers due*: May 12, 2017


*SUBMISSION GUIDELINES*


Abstracts of up to 500 words and a short bio of the author(s) should be 
emailed to (pictdc /at/ hawaii.edu) <mailto:(pictdc /at/ hawaii.edu)> by February 10, 
2017. Please include “Digital Inequalities ICA 2017” in the subject line.


Full papers accepted for presentation at the preconference will, with 
the consent of the authors, be submitted to the Journal of Information 
Policy(http://www.psupress.org/Journals/jnls_JIP.html/ 
<http://www.psupress.org/Journals/jnls_JIP.html/>) for consideration for 
a Special Issue curated by guest editors from the field. The papers will 
be blind peer-reviewed, to assure their academic value to both authors 
(for academic credit) and readers.







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