RESEARCH COLLOQUIUM: CALL FOR PAPERS
LEGAL AND ETHICAL ISSUES IN PREDICTIVE DATA ANALYTICS
June 19 & 20, 2014
Blacksburg, Va.
Abstract Submission Deadline: March 3, 2014
A research colloquium, “Legal and Ethical Issues in Predictive Data Analytics,” hosted by
Professor Janine Hiller of Virginia Tech and co-organized by Professor Tonia Hap Murphy of
the University of Notre Dame, is sponsored by the Center for Business Intelligence and
Analytics in the Pamplin College of Business, Virginia Tech.
Up to four invitations for research presentation slots will be extended based on this call for
papers. In order to receive consideration, researchers are invited to submit an abstract by
March 3, 2014.
TOPIC:
Various terms are used to describe the collection and use of data for decision-making. Big
data and data mining are two common terms that indicate the breadth and depth of data
sets and the subsequent use of that data to extract new meaning. Used here, data analytics
references a mathematical process that can discover trends, connections, and relationships
that may then feed into various models and processes. Furthermore, analytics implies that
data is used to make decisions that affect individuals or businesses based on the result of
algorithmic-derived predictions.
Legal scholarship related to the use of data analytics and its predictive application is sparse,
yet there are important questions that call for discussion. For example, a recurrent legal
issue in data management is the question of privacy rights and obligations for securing
information. Particular statutes protect health, financial, and children’s information to a
certain extent. Without a doubt, data analytics impacts privacy concerns more intensely and
in new ways; the meaning of individual privacy is challenged in an environment where new
personal insights are algorithmically “discovered” because of widely aggregated data and
analytic techniques. Zarsky describes the different types of data analysis and predictive
modeling in setting the background for a proposed transparency taxonomy for predictive
analytics. More broadly, Balkan notes that, “data mining technologies allow the state and
business enterprises to record perfectly innocent behavior that no one is particularly
ashamed of and draw surprisingly powerful inferences about people's behavior, beliefs, and
attitudes.”
As sophisticated data analysis and its predictive application becomes ubiquitous, what are
the legal and ethical considerations for society, business and government? What laws
protect individuals and corporations from data overreach and predictive applications?
What laws should be modified in order to reap the benefits of data analytics and predictive
modeling? What areas of commerce are most impacted? How are these questions being
addressed by different legal structures? Beyond legal questions, what ethical questions and
frameworks are important for the use of predictive analytics?
This colloquium seeks to promote research on the legal and ethical issues presented by the
predictive use of analytics in society, emphasizing uses by business entities.
Issues of interest include, but are not limited to, the following categories that are relevant to
the discussion of data analysis, predictive modeling, and decision-making processes:
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Privacy and data security
Consumer protection: price discrimination, targeting
Employment decisions and analytics
Corporate governance: decision-making, risk management, and oversight
Health analytics and ethics
Insurance: benefits, prices, structures
Intellectual property: data and analytic ownership, trade secret protection
Legal system: due process, e-discovery, evidentiary issues
Antitrust: collusion and data
International legal comparisons
Ethical use of predictive data analytics in commerce
National security: inferences, actions, and automation derived from data
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Submissions: To be considered, please submit an abstract of up to 750 words to Janine
Hiller at [email protected], and copied to [email protected] by March 3, 2014. Abstracts will
be evaluated based upon the quality of the abstract and the topic’s fit with other
presentations. Questions may be directed to Janine Hiller at [email protected] or Tonia Hap
Murphy at [email protected]
Those submitting abstracts will be informed of the outcome by March 17, 2014. If accepted,
the author agrees to submit a discussion paper of 7,500 to 10,000 words by June 2, 2014.
Formatting will be either APA or Bluebook. The paper need not be in final form; however, it
should be complete enough to benefit from and elicit discussion at the colloquium. In the
case of papers with multiple authors, only one author may attend and present at the
colloquium.
The organizers are negotiating publication options for colloquium papers, with the goal of
producing a scholarly book or special journal issue. While a final decision is pending, we
are firmly optimistic about an ultimate publication. Authors agree to submit their final
papers to be edited for inclusion in such a future publication. It is anticipated that final
submissions and editing will occur in the fall of 2014.
Colloquium Details:
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The colloquium will begin at noon on June 19th and conclude at the end of the day on
June 20th 2014.
Approximately 50 minutes is allotted for discussion of each paper.
Presenters will submit discussion manuscripts by June 2, 2014.
The manuscripts will be posted in a password protected members only forum
online. Each participant agrees to read and be prepared to participate in discussions
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of papers. Participants may be asked to lead discussion of one other submitted
paper.
Participants will be provided with lodging and meals during the conference dates,
but are responsible for transportation to Virginia Tech.
SELECTED REFERENCES:
Jack M. Balkin, The Constitution in the National Surveillance State, 93 Minn. L. Rev. 1, 2
(2008).
Kenneth A. Bamberger, Technologies of Compliance: Risk and Regulation in a Digital Age, 88
Tex. L. Rev. 669 (2010).
Big Data and Analytics: Seeking Foundations for Effective Privacy Guidance,
http://www.hunton.com/files/Uploads/Documents/News_files/Big_Data_and_Analytics_Fe
bruary_2013.pdf
(for further information about the participants and project see
http://www.informationpolicycentre.com/guidance_for_analytics/)
Kate Crawford & Jason Schultz, Big Data and Due Process: Toward a Framework to Redress
Predictive Privacy Harms, forthcoming in 55 B.C.L.Rev. – (2014).
Multiple Authors, Privacy and Big Data: Making Ends Meet, Stanford Law Review
Perspectives (see for discussion of multiple issues)
http://www.stanfordlawreview.org/online/privacy-and-big-data
Tonia Hap Murphy, Mandating Use of Predictive Coding in Electronic Discovery: An Ill-Advised
Judicial Intrusion, 50 Am. Bus. L.J. 609 (2013).
Charles Nyce, Predictive Analytics White Paper (AICPCU/IIA), available at
http://www.theinstitutes.org/doc/predictivemodelingwhitepaper.pdf.
Paul M. Schwartz, Data Protection Law and the Ethical Use of Analytics, available at
http://www.informationpolicycentre.com/ethical_underpinnings_analytics/
SEC Announces Enforcement Initiatives to Combat Financial Reporting and Micro Fraud and
Enhance Risk Analysis
http://www.sec.gov/News/PressRelease/Detail/PressRelease/1365171624975#.UqiIUI0X
h-M
Eric Siegel, PREDICTIVE ANALYTICS: THE POWER TO PREDICT WHO WILL CLICK, BUY, LIE, OR DIE
(2013).
Omer Tene & Jules Polonetsky, Big Data for All: Privacy and User Control in the Age of
Analytics, 11 Nw. J. Tech. & Intell. Prop. 239 (2013).
Tal Z. Zarsky, Transparent Predictions, 2013 U. Ill. L. Rev. 1503 (2013).
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