Accepted Paper:

Critical Information Practice  

Authors:

Yanni Loukissas (Georgia Institute of Technology)
Matt Ratto (University of Toronto)
Gabby Resch (University of Toronto)

Paper short abstract:

A pedagogical model grounded in interpretive learning experiences: collecting data from messy sources, processing data with an eye towards what algorithms occlude, and presenting data through creative forms like narrative and sculpture.

Paper long abstract:

Big Data has been described as a death knell for the scientific method (Anderson, 2008), a catalyst for new epistemologies (Floridi, 2012), a harbinger for the death of politics (Morozov, 2014), and "a disruptor that waits for no one" (Maycotte, 2014). Contending with Big Data, as well as the platitudes that surround it, necessitates new kind of data literacy. Current pedagogical models, exemplified by data science and data visualization, too often introduce students to data through sanitized examples, black-boxed algorithms, and standardized templates for graphical display (Tufte, 2001; Fry, 2008; Heer, 2011). Meanwhile, these models overlook the social and political implications of data in areas like healthcare, journalism and city governance. Scholarship in critical data studies (boyd and Crawford, 2012; Dalton and Thatcher, 2014) and critical visualization (Hall, 2008; Drucker 2011) has established the necessary foundations for an alternative to purely technical approaches to data literacy. In this paper, we explain a pedagogical model grounded in interpretive learning experiences: collecting data from messy sources, processing data with an eye towards what algorithms occlude, and presenting data through creative forms like narrative and sculpture. Building on earlier work by the authors in the area of 'critical making' (Ratto), this approach—which we call critical information practice—offers a counterpoint for students seeking reflexive and materially-engaged modes of learning about the phenomenon of Big Data.

Panel T113
Critical data studies