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

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.

Synthesizing a novel genetic sequential logic circuit: a push-on push-off switch.

Design and synthesis of basic functional circuits are the fundamental tasks of synthetic biologists. Before it is possible to engineer higher-order genetic networks that can perform complex functions, a toolkit of basic devices must be developed. Among those devices, sequential logic circuits are expected to be the foundation of the genetic information-processing systems. In this study, we report the design and construction of a genetic sequential logic circuit in Escherichia coli. It can generate different outputs in response to the same input signal on the basis of its internal state, and 'memorize' the output. The circuit is composed of two parts: (1) a bistable switch memory module and (2) a double-repressed promoter NOR gate module. The two modules were individually rationally designed, and they were coupled together by fine-tuning the interconnecting parts through directed evolution. After fine-tuning, the circuit could be repeatedly, alternatively triggered by the same input signal; it functions as a push-on push-off switch.