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

By Daizhuo Chen, Samuel P. Fraiberger, Robert Moakler, Foster J. Provost. 2016

41 citations recorded by the source corpus.

Abstract Recent studies show the remarkable power of fine-grained information disclosed by users on social network sites to infer users' personal characteristics via predictive modeling. Similar fine-grained data are being used successfully in other commercial applications. In response, attention is turning increasingly to the transparency that organizations 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. In this article, we focus on inferences about personal characteristics based on information disclosed by users' online actions. As a use case, we explore personal inferences that are made possible from “Likes” on Facebook. We first present a means for providing transparency into the information responsible for inferences drawn by data-driven models. We then introduce the “cloaking device”—a mechanism for users to inhibit the use of particular pieces of information in inference. Using these analytical tools we ask two main questions: (1) How much information must users cloak to significantly affect inferences about their personal traits? We find that usually users must cloak only a small portion of their actions 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. We demonstrate a simple modeling change that 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 control easier or harder for their users.

Subfields: Marketing, Sociology and Political Science, Statistical and Nonlinear Physics, Privacy & Security, AI Ethics, Fairness & Society

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Marketing

1. Nadia Burkart
2. Marco F. Huber

In Sociology and Political Science

1. Supriyo Chakraborty
2. Nadia Burkart
3. Marco F. Huber

In Statistical and Nonlinear Physics

1. Supriyo Chakraborty
2. Nadia Burkart
3. Marco F. Huber

In Privacy & Security

1. Supriyo Chakraborty
2. Nadia Burkart
3. Marco F. Huber

In AI Ethics, Fairness & Society

1. Supriyo Chakraborty
2. Nadia Burkart
3. Alun David Preece
4. Marco F. Huber

Citations in the corpus

• Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems (2018, background): “…companies, data-subjects may become more likely to exercise these rights and ask organizations to delete some or all of their data, with the goal of increasing their privacy — see, e.g., (Chen et al., 2016) for a study analyzing Facebook’s predictions about users before and after data deletion.”
• A Survey on the Explainability of Supervised Machine Learning (2020, methodology): “(2012) CN2 Classification Rule-based Clark and Niblett (1989) RIPPER Classification Rule-based Cohen (1995) Re-RX Classification Rule-based Setiono et al. (2008) C5.”