Paper short abstract:

Reconstructing the epistemological and ontological assumptions of big data analysis as put forward by its key proponents, this paper argues that the rise of big data and data-based learning algorithms can be understood as a Eurocentric and androcentric project.

Paper long abstract:

This paper argues that the rise of big data and data-based learning algorithms can be understood as a Eurocentric and androcentric project. The argument is unfolded in three steps: First, it is shown that the epistemological and ontological assumptions underlying big data analysis draw on an understanding of objectivity that has been established in 18th and 19th-century science. Second, this modern understanding of objectivity as neutral observation 'from nowhere' is shown to be inextricably linked with the constitution of the modern subject of reason as a 'white', masculine subject. Based on these first two analytical steps it is then argued that there are important historic continuities in the development of big data and data-based learning algorithms that are easily overlooked when considering big data as radically new technological development. These continuities point at big data analysis and data-based learning algorithms as the current manifestation of a logic of reasoning with specifically modern roots.

The empirical basis for this argumentation are statements on and assumptions about big data analysis that have been made publically available by key actors and proponents in the field. Theoretically the argumentation is foremost inspired by feminist technoscience, but also critical data and algorithm studies and social studies of numbers and accounting.