Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Epistemic values in feature importance methods: Lessons from feminist
  epistemology   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.

14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
8
The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
7
The categories of this paper are: hep-th
16
The categories of this paper are: stat
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The categories of this paper are: q-fin
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
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The categories of this paper are: dg-ga
20
The categories of this paper are: acc-phys