  As the public seeks greater accountability and transparency from machine
learning algorithms, the research literature on methods to explain algorithms
and their outputs has rapidly expanded. Feature importance methods form a
popular class of explanation methods. In this paper, we apply the lens of
feminist epistemology to recent feature importance research. We investigate
what epistemic values are implicitly embedded in feature importance methods and
how or whether they are in conflict with feminist epistemology. We offer some
suggestions on how to conduct research on explanations that respects feminist
epistemic values, taking into account the importance of social context, the
epistemic privileges of subjugated knowers, and adopting more interactional
ways of knowing.
