Accepted Paper:

Big data and the mythology of algorithms  


Howard Rosenbaum (Indiana University)

Paper short abstract:

Big data relies on algorithms, which are typically presented as objective and unbiased. They are not. As they become more deeply entangled in our lives, it is important to understand the implications of the roles they are playing. This paper critically analyzes this mythology of algorithms.

Paper long abstract:

There are no big data without algorithms. Algorithms are sociotechnical constructions and reflect the social, cultural, technical and other values embedded in their contexts of design, development, and use. The utopian "mythology" (boyd and Crawford 2011) about big data rests, in part, on the depiction of algorithms as objective and unbiased tools operating quietly in the background. As reliable technical participants in the routines of life, their impartiality provides legitimacy for the results of their work. This becomes more significant as algorithms become more deeply entangled in our online and offline lives. where we generate the data they analyze. They create "algorithmic identities," profiles of us based on our digital traces that are "shadow bodes," emphasizing some aspects and ignoring others (Gillespie 2012). They are powerful tools that use these identities to dynamically shape the information flows on which we depend in response to our actions and decisions made by their owners

Because this perspective tends to dominate the discourse about big data, thereby shaping public and scientific understandings of the phenomenon, it is necessary to subject it to critical review as an instance if critical data studies. This paper interrogates algorithms as human constructions and products of choices that have a range of consequences for their users and owners; issues explored include:

The epistemological implications of big data algorithms

The impacts of these algorithms in our social and organizational lives

The extent to which they encode power ways in which this power is exercised

The possibility of algorithmic accountability

Panel T113
Critical data studies