A Plea for History and Philosophy of Statistics and Machine Learning—With a Little Story of Achievabilism

Hanti Lin (University of California, Davis)

Abstract: The integration of the history and philosophy of statistics was initiated at least by Hacking (1965) and advanced by Mayo (1996), but it has received little sustained follow-up. Yet such integration is more urgent than ever, as the recent success of artificial intelligence has been driven largely by machine learning—a field historically developed alongside statistics. Today, the boundary between statistics and machine learning is increasingly blurred. What we now need is integration on two levels: an integrated history and philosophy of an integrated field—statistics and machine learning. I present a case study of a philosophical idea in machine learning whose roots can be traced back to an often underappreciated insight in Neyman and Pearson’s work of the 1930s. This leads to the articulation of a foundational assumption—largely implicit in, but shared by, the practices of frequentist statistics and machine learning—which I call achievabilism. A third integration also emerges at the level of methodology: combining formal epistemology (FE) with history and philosophy of science (HPS).

Zoom Link:

https://rutgers.zoom.us/j/99554502801?pwd=rXCCcpqG9LU TdvBDaZIOqnDbp0wFS9.1