  In the ML fairness literature, there have been few investigations through the
viewpoint of philosophy, a lens that encourages the critical evaluation of
basic assumptions. The purpose of this paper is to use three ideas from the
philosophy of science and computer science to tease out blind spots in the
assumptions that underlie ML fairness: abstraction, induction, and measurement.
Through this investigation, we hope to warn of these methodological blind spots
and encourage further interdisciplinary investigation in fair-ML through the
framework of philosophy.
