The epistemological foundations of data science: a critical analysis

Abstract

The modern abundance and prominence of data has led to the development of “data science” as
a new field of enquiry, along with a body of epistemological reflections upon its foundations,
methods, and consequences. This article provides a systematic analysis and critical review of
significant open problems and debates in the epistemology of data science. We propose a partition
of the epistemology of data science into the following five domains: (i) the constitution of data
science; (ii) the kind of enquiry that it identifies; (iii) the kinds of knowledge that data science
generates; (iv) the nature and epistemological significance of “black box” problems; and (v) the
relationship between data science and the philosophy of science more generally.

Keywords: Black box; Data-driven science; Data science; Epistemology; Foundationalism; Philosophy of science.