In response to the COVID-19 pandemic, there has been a rapid growth in research focused on developing vaccines and therapies. In this context, the need for speed is taken for granted, and the scientific process has adapted to accommodate this. On the surface, attempts to speed up the research enterprise appear to be a good thing. It is, however, important to consider what, if anything, might be lost when biomedical innovation is sped up. In this article we use the case (...) of a study recently retracted from the Lancet to illustrate the potential risks and harms associated with speeding up science. We then argue that, with appropriate governance mechanisms in place, it should be quite possible to both speed up science and remain attentive to scientific quality and integrity. (shrink)

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. (shrink)