Big Data and the Paradox of Diversity
Author(s): Rieder, Bernhard
This paper develops a critique of Big Data and associated analytical techniques by focusing not on errors – skewed or imperfect datasets, false positives, underrepresentation, and so forth – but on data mining that works. After a quick framing of these practices as interested readings of reality, I address the question of how data analytics and, in particular, machine learning reveal and operate on the structured and unequal character of contemporary societies, installing “economic morality” (Allen 2012) as the central guiding principle. Rather than critiquing the methods behind Big Data, I inquire into the way these methods make the many differences in decentred, non-traditional societies knowable and, as a consequence, ready for profitable distinction and decision-making. The objective, in short, is to add to our understanding of the “profound ideological role at the intersection of sociality, research, and commerce” (van Dijck 2014: 201) the collection and analysis of large quantities of multifarious data have come to play. Such an understanding needs to embed Big Data in a larger, more fundamental critique of the societal context it operates in.
Rieder, Bernhard: Big Data and the Paradox of Diversity. In: Digital Culture & Society, Jg. 2 (2016), Nr. 2, S. 39–54. DOI: https://doi.org/10.25969/mediarep/977.
Except where otherwise noted, this item's license is described as Creative Commons - Attribution - Non Commercial - No Derivatives