Visualization Personal Data for Infrastructural Inversion

• In Event: Seeing through New Eyes: Science, Truth and Technologies of Visualization - II

Abstract

We live in an era of massive and pervasive collection of personal data to feed algorithms for marketing, personalization, policing, and other forms of prediction and surveillance. At the same time, it is almost impossible for individuals to know the extent of data collection to which they are subjected, or to know the extent of data use by the organizations and institutions that are collecting this data. The information infrastructures that support this personal data economy are, for the most part, opaque to end users. Drawing on two examples, this paper will frame visualization as a tool for inciting infrastructural inversion. Rather than attempting to reveal algorithmic infrastructures directly, these visualization examples – one aimed at algorithmic filtering of news feeds and one focused on processes of data driven inference for marketing – stimulate reflection on underlying algorithmic processes by creating friction, surprise, or a sense of the absurd. To the extent that infrastructures become visible upon breakdown (Star & Ruhleder, 1996), this paper explores the potential for visualization to reveal breakdown in order to generate infrastructural inversion. The paper also considers the potential for using these kinds of visualizations within policy-making contexts or to encourage everyday civic engagement.

Author

• Matthew Bietz, University of California Irvine