How Tech Billionaires Are Building a Post-Democratic America — And Why Europe Is Next

This paper contends with the notion that the methods of machine learning (ML) are unique among the tools of science in enabling a form of theory-free inductive inference. I challenge these assertions of epistemic distinctness, attributing the prevalence of these views to an untenable conception of scientific objectivity: what I term a theory-free ideal, in homage to its normative counterpart. ML, as a formal method of induction, must rely on conceptual or theoretical resources to get inference off the ground. By means of two case studies, I argue that this theory-free ideal has a deleterious effect on the epistemic standing of ML-involving science.

Patrick Anthony (Uppsala University) and Ghada Dimashk (American University of Beirut) Baba, when will we study astronomy in school? … I want to learn about the moon and stars. (Banias, age 9, quoted ...

TWELVE countries have agreed to take concrete action against Israel to stop its genocide against the Palestinian people ...

In this article, I sketch out the contours of a political philosophy of experimentation. Drawing on the work of Joseph Rouse, Hans Radder, Gilbert Hottois, and Jerome Ravetz, I argue that such a polit...