Societal Computing

Social Computing research focuses on online social behavior and using artifacts derived from it for providing recommendations and other useful community knowledge. Unfortunately, some of that behavior and knowledge incur societal costs, particularly with regards to Privacy, which is viewed quite differently by different populations as well as regulated differently in different locales. But clever technical solutions to those challenges may impose additional societal costs, e.g., by consuming substantial resources at odds with Green Computing, another major area of societal concern. We propose a new crosscutting research area, Societal Computing, that focuses on the technical tradeoffs among computational models and application domains that raise significant societal issues. This dissertation, advised by Prof. Gail Kaiser, will focus on privacy concerns in the context of Societal Computing and will aim to address research topics such as design patterns and architectures for privacy tradeoffs, better understanding of users' privacy requirements so that tradeoffs with other areas such as green computing can be dealt with in a more effective manner, and better visualization techniques for making privacy and its tradeoffs more understandable.