The politics of data science. Institutionalising algorithmic regimes of knowledge production in academia
Sprache des Vortragstitels:
EASST 2022. Politics of Technoscientific Futures
Sprache des Tagungstitel:
Closed Panel 15. Algorithmic regimes: interactions, politics, and methods, Session 1. This paper focuses on the rise of Data Science within academia, in order to study the institutionalisation of a new regime of knowledge production that is based on algorithmic big data analysis. Following Foucault?s (1978) concept of power/knowledge, we (Bianca Prietl, Stefanie Raible) take questions of epistemology as inextricably linked to questions of power, and hence, ask how Data Science are structurally implemented, epistemologically positioned, and discursively legitimised in the course of the ongoing process of their institutionalisation within academia. Based on an empirical research project that studies the establishment of academic Data Science in German-speaking countries, i.e. Austria, Germany, and Switzerland, we (Bianca Prietl, Stefanie Raible) depict some key features of this development that is crucial for understanding the power dynamics incorporated within the larger shifts in society?s regime of truth: Structurally Data Science chairs as well as study programs become primarily implemented within STEM departments, and scholars as well as students are mainly recruited from computer science, rendering data analysis an object of computer science. Epistemologically Data Science are presented as an ?analytical tool-kit? that allows for analysing data in (almost) every so-called ?domain?. Thus, Data Science claim to offer a universal approach to knowledge production, and decision-making. Important modes of legitimising Data Science are i.a. the postulation of a given and unchangeable datafication and digitalisation of society enabled by a technological state of the art that must be utilised for science?s and society?s sake. Thereby, Data Science are positioned as offering a key to more information, better decision, as well as innovation in various academic and non-academic fields (e.g. medicine or private corporations).