University of Pennsylvania ScholarlyCommons Publicly Accessible Penn Dissertations 1-1-2016 Data Epistemologies / Surveillance and Uncertainty Sun Ha Hong University of Pennsylvania,
[email protected] Follow this and additional works at: http://repository.upenn.edu/edissertations Part of the Communication Commons, Other Sociology Commons, and the Philosophy of Science Commons Recommended Citation Hong, Sun Ha, "Data Epistemologies / Surveillance and Uncertainty" (2016). Publicly Accessible Penn Dissertations. 1766. http://repository.upenn.edu/edissertations/1766 This paper is posted at ScholarlyCommons. http://repository.upenn.edu/edissertations/1766 For more information, please contact
[email protected]. Data Epistemologies / Surveillance and Uncertainty Abstract Data Epistemologies studies the changing ways in which ‘knowledge’ is defined, promised, problematised, legitimated vis-á-vis the advent of digital, ‘big’ data surveillance technologies in early twenty-first century America. As part of the period’s fascination with ‘new’ media and ‘big’ data, such technologies intersect ambitious claims to better knowledge with a problematisation of uncertainty. This entanglement, I argue, results in contextual reconfigurations of what ‘counts’ as knowledge and who (or what) is granted authority to produce it – whether it involves proving that indiscriminate domestic surveillance prevents terrorist attacks, to arguing that machinic sensors can know us better than we can ever know ourselves. The present work focuses on two empirical cases. The first is the ‘Snowden Affair’ (2013-Present): the public controversy unleashed through the leakage of vast quantities of secret material on the electronic surveillance practices of the U.S. government. The es cond is the ‘Quantified Self’ (2007-Present), a name which describes both an international community of experimenters and the wider industry built up around the use of data- driven surveillance technology for self-tracking every possible aspect of the individual ‘self’.