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

Methodological Blind Spots in Machine Learning Fairness: Lessons from the Philosophy of Science and Computer Science