Methodological Blind Spots in Machine Learning Fairness: Lessons from the Philosophy of Science and Computer Science
It addresses fairness issues in ML for researchers and practitioners, but is incremental as it builds on existing philosophical critiques without introducing new technical solutions.
The paper tackles methodological blind spots in machine learning fairness by applying ideas from philosophy of science and computer science, such as abstraction, induction, and measurement, to critically evaluate underlying assumptions and encourage interdisciplinary research.
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.