My research program is focused on understanding and finding solutions to problems that arise in sociotechnical systems as they become an increasingly invisible and indispensable part of everyday life. A sociotechnical system involves people, technology, and information; these parts all interact and influence each other, and without all three parts the system would not function as it should. These systems have great potential to help people and improve their lives; however, they also have the potential for harm. In particular, I focus on sociotechnical systems that are “black boxes” from the perspective of people using the system—the inputs and outputs can be observed, but the inner workings can’t be and are therefore hard for people to understand.
Lately, I have been working on how to help users manage the privacy social dilemma that arises when algorithms in ubiquitous computing systems make new inferences from the data they collect to help the system work better, but that users might not want to disclose. For example, data collected by technologies like Fitbit activity trackers and digital assistants like Amazon’s Alexa or the Google Home that is is stored in the cloud can be aggregated and analyzed in order to improve the functionality and services the systems provide. But, the same data might also be used to infer sensitive personal information about the people using these systems that they might want to keep to themselves. This project aims to identify norms for the use of derived data in a ubiquitous computing system, and design and evaluate a mechanism for coordination among users of the system such that they can jointly manage the derived data as a common-pool resource.
My research is currently funded by NSF SaTC award CNS-1524296, and by an endowment to MSU from AT&T. Some keywords to describe my research interests are: algorithmic curation, personalization, automation, information privacy, derived data, ubiquitous computing, sociotechnical systems, user-contributed content, human computer interaction.