  Machine learning is currently involved in some of the most vigorous debates
it has ever seen. Such debates often seem to go around in circles, reaching no
conclusion or resolution. This is perhaps unsurprising given that researchers
in machine learning come to these discussions with very different frames of
reference, making it challenging for them to align perspectives and find common
ground. As a remedy for this dilemma, we advocate for the adoption of a common
conceptual framework which can be used to understand, analyze, and discuss
research. We present one such framework which is popular in cognitive science
and neuroscience and which we believe has great utility in machine learning as
well: Marr's levels of analysis. Through a series of case studies, we
demonstrate how the levels facilitate an understanding and dissection of
several methods from machine learning. By adopting the levels of analysis in
one's own work, we argue that researchers can be better equipped to engage in
the debates necessary to drive forward progress in our field.
