Knowledge’s Allure: Surveillance and Uncertainty

The present age is one of growing faith in machinic knowledge. From state surveillance to self-tracking technologies, we find lofty promises about the power of “raw” data, sensing machines and algorithmic decision-making. But new claims to knowledge invariably entail a redistribution of uncertainty, of those in the know and those left ignorant, of proofs “good enough” and “negligible” risks. Today, the U.S. government struggles to “prove” the efficacy of its own surveillance programs. The calculability of terrorist threat becomes profoundly indeterminable, exemplified by the figure of the “lone wolf”. Meanwhile, the self-tracking industry promises unerringly objective self-knowledge through machines that know you better than you know yourself. The present struggles with “big” data and surveillance are not just a question of privacy and security, but how promises of knowledge and its bounty enact a redistribution of authority, credibility and responsibility. In short, it is a question of how human individuals become the ingredient for the production of truths and judgments about them by things other than themselves.

Sun-ha Hong is a Mellon Postdoctoral Fellow in the Humanities at CMS/W @ MIT, and has a Ph.D. from the Annenberg School for Communication, University of Pennsylvania. His writing examines the collective fantasies invested in technology, media and communication.