Businesses and governments believe in "Big Data" tools for surveillance, management, marketing etc. But what do we know of the practices, effects, public perceptions of Big Data systems? This track will consist of empirical and theoretical papers addressing various aspects of the theme of Big Data.
As computing power has increased and prices decreased (remember Moore's Law?), visions of surveillance through the analysis of vast collections of data have been translated into social and material reality. But what do we know of that reality? What data are being collected, stored and analyzed, by whom, and for what purposes? How accurate are the algorithms? How securely are the data stored? What are the real-life consequences of false positives and false negatives, and how frequently do these occur? For that matter, what are the real-life consequences of "true" positives and negatives, or of data leaks? How have visions of Big Data surveillance changed? What visions are being invoked in which contexts? How (if at all) are Big Data practices regulated? What possibilities exist for resistance and/or evasion at grass roots level? Is there any hold in the argument - put forth by amongst others US Senator Dianne Feinstein, chair of the Senate Intelligence Committee - that NSA's massive collection of communications content and meta-data is "not a surveillance program, it is a data-collection program."? Empirical and theoretical papers addressing any aspect of this theme are invited and will be sorted into a cohesive (series of) session(s) by the convener in collaboration with submitters. The papers will be presented in the order shown and grouped 4-4-4-4-4 between sessions