In June 2013, United States (US) government contractor Edward Snowden arranged for journalists at The Guardian to release classified information detailing US government surveillance programs. While this release caused the public to decry the scope and privacy concerns of these surveillance systems, Snowden's actions also caused the US Congress to critique how Snowden got a security clearance allowing him access to sensitive information in the first place. Using Snowden's actions as a kairotic moment, this study examined congressional policy documents through a qualitative content analysis to identify what Congress suggested could “fix” in the background investigation (BI) process. The study then looked at the same documents to problematize these “solutions” through the terministic screen of surveillance studies.

By doing this interdisciplinary rhetorical analysis, the study showed that while Congress encouraged more oversight, standardization, and monitoring for selected steps of the BI process, these suggestions are not neutral solutions without larger implications; they are value-laden choices which have consequences for matters of both national security and social justice. Further, this study illustrates the value of incorporating surveillance as framework in rhetoric, composition, and professional/technical communication research.

This dissertation research investigates the social implications of computing artifacts that make use of sensor driven self-quantification to implicitly or explicitly direct user behaviors. These technologies are referred to here as self-sensoring prescriptive applications (SSPA’s). This genre of technological application has a strong presence in healthcare as a means to monitor health, modify behavior, improve health outcomes, and reduce medical costs. However, the commercial sector is quickly adopting SSPA’s as a means to monitor and/or modify consumer behaviors as well (Swan, 2013). These wearable devices typically monitor factors such as movement, heartrate, and respiration; ostensibly to guide the users to better or more informed choices about their physical fitness (Lee & Drake, 2013; Swan, 2012b). However, applications that claim to use biosensor data to assist in mood maintenance and control are entering the market (Bolluyt, 2015), and applications to aid in decision making about consumer products are on the horizon as well (Swan, 2012b). Interestingly, there is little existing research that investigates the direct impact biosensor data have on decision making, nor on the risks, benefits, or regulation of such technologies. The research presented here is inspired by a number of separate but related gaps in existing literature about the social implications of SSPA’s. First, how SSPA’s impact individual and group decision making and attitude formation within non-medical-care domains (e.g. will a message about what product to buy be more persuasive if it claims to have based the recommendation on your biometric information?). Second, how the design and designers of SSPA’s shape social behaviors and third, how these factors are or are not being considered in future design and public policy decisions.