A sound and effective data ethics requires an independent and mature epistemology of data science. We cannot address the ethical risks associated with data science if we cannot effectively diagnose its epistemological failures, and this is not possible if the outcomes, methods, and foundations of data science are themselves immune to criticism. An epistemology of data science that guards against the unreflective reliance on data science blocks this immunity. Critical evaluations of the epistemic significance of data and of the impact of design-decisions in software engineering already contribute to this enterprise but leave the role of mathematics within data science largely unexamined. In this chapter we take a first step to fill this gap. In a first part, we emphasise how data, code, and maths jointly enable data science, and how they contribute to the epistemic and scientific respectability of data science. This analysis reveals that if we leave out the role of mathematics, we cannot adequately explain how epistemic success in data science is possible. In a second part, we consider the more contentious dual issue: Do explanations of epistemic failures in data science also force us to critically assess the role of maths in data science? Here, we argue that mathematics not only contributes mathematical truths to data science, but also substantive epistemic values. If we evaluate these values against a sufficiently broad understanding of what counts as epistemic success and failure, our question should receive a positive answer.
|Title of host publication||The 2018 Yearbook of the Digital Ethics Lab|
|Editors||Öhman Carl, Watson David|
|Number of pages||16|
|Publication status||Published - 2019|