Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Identifying Biased Subgroups in Ranking and Classification   When analyzing the behavior of machine learning algorithms, it is important
to identify specific data subgroups for which the considered algorithm shows
different performance with respect to the entire dataset. The intervention of
domain experts is normally required to identify relevant attributes that define
these subgroups.
  We introduce the notion of divergence to measure this performance difference
and we exploit it in the context of (i) classification models and (ii) ranking
applications to automatically detect data subgroups showing a significant
deviation in their behavior. Furthermore, we quantify the contribution of all
attributes in the data subgroup to the divergent behavior by means of Shapley
values, thus allowing the identification of the most impacting attributes.

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The categories of this paper are: chem-ph
36
The categories of this paper are: cmp-lg
19
The categories of this paper are: eess
22
The categories of this paper are: chao-dyn
20
The categories of this paper are: acc-phys
25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
5
The categories of this paper are: gr-qc
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
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The categories of this paper are: nlin
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The categories of this paper are: mtrl-th
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The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
17
The categories of this paper are: q-fin