Enhancing Transparency and Control When Drawing Data-Driven Inferences About Individuals

25 Pages Posted: 28 May 2015

See all articles by Daizhuo Chen

Daizhuo Chen

Columbia University

Samuel P. Fraiberger

Computer Science; Harvard University - Institute for Quantitative Social Sciences; Northeastern University - Network Science Institute

Robert Moakler

New York University (NYU) - Leonard N. Stern School of Business

Foster Provost

New York University

Date Written: May 2015

Abstract

Recent studies show the remarkable power of information disclosed by users on social network sites to infer the users' personal characteristics via predictive modeling. In response, attention is turning increasingly to the transparency that sites provide to users as to what inferences are drawn and why, as well as to what sort of control users can be given over inferences that are drawn about them. We draw on the evidence counterfactual as a means for providing transparency into why particular inferences are drawn about them. We then introduce the idea of a \cloaking device" as a vehicle to provide (and to study) control. Specifically, the cloaking device provides a mechanism for users to inhibit the use of particular pieces of information in inference; combined with the transparency provided by the evidence counterfactual a user can control model-driven inferences, while minimizing the amount of disruption to her normal activity. Using these analytical tools we ask two main questions: (1) How much information must users cloak in order to significantly affect inferences about their personal traits? We find that usually a user must cloak only a small portion of her actions in order to inhibit inference. We also find that, encouragingly, false positive inferences are significantly easier to cloak than true positive inferences. (2) Can firms change their modeling behavior to make cloaking more difficult? The answer is a definitive yes. In our main results we replicate the methodology of Kosinski et al. (2013) for modeling personal traits; then we demonstrate a simple modeling change that still gives accurate inferences of personal traits, but requires users to cloak substantially more information to affect the inferences drawn. The upshot is that organizations can provide transparency and control even into complicated, predictive model-driven inferences, but they also can make modeling choices to make control easier or harder for their users.

Suggested Citation

Chen, Daizhuo and Fraiberger, Samuel P. and Moakler, Robert and Provost, Foster, Enhancing Transparency and Control When Drawing Data-Driven Inferences About Individuals (May 2015). NYU Working Paper No. 2451/33969. Available at SSRN: https://ssrn.com/abstract=2611542

Daizhuo Chen (Contact Author)

Columbia University

3022 Broadway
New York, NY 10027
United States

Samuel P. Fraiberger

Computer Science ( email )

60 5th Avenue
New York, NY 10012
United States

Harvard University - Institute for Quantitative Social Sciences ( email )

1737 Cambridge St
Cambridge, MA 02115
United States

Northeastern University - Network Science Institute ( email )

177 Huntington Avenue
Boston, MA 02115
United States

Robert Moakler

New York University (NYU) - Leonard N. Stern School of Business

44 West 4th Street
Suite 9-160
New York, NY NY 10012
United States

Foster Provost

New York University ( email )

44 West Fourth Street
New York, NY 10012
United States

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