Precision and Uncertainty in a World of Data - II

Abstract

Precision and Uncertainty in a World of Data brings the varied disciplines of STS to bear on the variegated nature of uncertainty produced within a data saturated environment. Our aim is to examine how past and present invocations of big data, which hold out the promise of precision and certainty, also proliferate uncertainties within many domains of practice: from medicine to marketing, criminal law to news media, and across almost all scientific fields. We seek new lines of inquiry into the challenges posed to scientific inquiry and social institutions by the consolidation of computational analysis, machine learning and the generation of big data. Papers should cohere around the dialectic of certainty and uncertainty produced by big data and algorithms in practice, with particular interest in four themes: (1) the computational turn in the sciences (what new logics of uncertainty within contemporary data practices will govern scientific inquiry in the future?) (2) shared reality and mis-information (how do presumptions of errors, mistakes, mis-information secrete into everyday life, and manifest as rumors, fears of falsity, and data theft, and raise questions as to whether we partake in a shared reality or live in alternate ones?) (3) the speculative imagination (how do data driven fields of study and analysis, consciously or unconsciously draw upon traditions of futurology in imagining threats and promise of the datafication of self and society?) and (4) citizens and publics (how are new subjectivities and novel spaces for engagement/disengagement articulated as data driven practices in everyday life?).

Chair

• Steve Sturdy

Discussant

• Steve Sturdy

Individual Presentations

• Modes of Uncertainty: Negotiating Flood Risk Between Engineers, Bureaucrats, and Publics - Robert Soden

• Representing Electricity: Economics, Physics, and Computing - Canay Ozden-Schilling, Johns Hopkins University

• The Screw, the Bug and the Schemer: Uncertainty Resulting from Infrastructural Ruptures and Social Cuts in Data-Driven High-Energy Physics - Anne Dippel, Friedrich Schiller University of Jena

• The Unlikely Introduction of Algorithmic Prediction in Environmental Policy: Learning from the ToxCast Puzzle - David Demortain, INRAE, LISIS

• Introducing the Data-Oriented Approach for Earthquake Disaster Prevention Policy in Japan: Prospects and Challenges - Noel Kikuchi, National Graduate Institute for Policy Studies; Keiko Matsuo, Japan Science and Technology Agency; Yasushi Sato, Niigata University

Session Organizer

• Jeremy A Greene, Johns Hopkins University