Mobile phones could become the largest surveillance system on the planet. These ubiquitous devices can sense and record data such as images, sound and location. They can automatically upload this data via wireless connections into systems for aggregation and analysis. But unlike traditional surveillance devices, phone sensors can be controlled by billions of individuals around the world. Are emerging mobile technologies platforms for citizen participation in research and discovery? Or new tools for mass surveillance?
Location-based technologies and mobile phone applications like carbon footprint calculator Ecorio and Google’s Latitude are attracting attention and raising new questions for engineers, policy makers, and users. These systems collect and combine data in new ways, and their effects cross political boundaries. Who will build and control processes such as data storage, aggregation, sharing, and retention? What policies are required to control this data, and who sets them? And to what purposes will these systems be deployed?
Humanists, social scientists, and technologists all have tools and perspectives to investigate these questions and contribute to a discussion of social issues in mobile sensing. This course brings together students from across campus to use some of those disciplinary tools and explore ethics and social challenges engendered by new technologies. Readings, discussion, design exercises and assignments will provide methods, tools, and contexts for unpacking the social issues embedded in emerging technologies. We will concentrate on the features of mobile technologies and how our worldview – specific cultural lenses, research practices, political orientations, economic pressures, popular narratives and fiction – influences how these features are imagined and built.
Shilton, Katie, "Mobile Technologies: Participation and Surveillance" (2010). Ethics in Science and Engineering National Clearinghouse. Paper 400.
Library and Information Science | Science and Technology Studies
Acknowledgement and Disclaimer
Funding for this project comes from the National Science Foundation through grant number 0832873.
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