Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Toward Best Practices for Explainable B2B Machine Learning   To design tools and data pipelines for explainable B2B machine learning (ML)
systems, we need to recognize not only the immediate audience of such tools and
data, but also (1) their organizational context and (2) secondary audiences.
Our learnings are based on building custom ML-based chatbots for recruitment.
We believe that in the B2B context, "explainable" ML means not only a system
that can "explain itself" through tools and data pipelines, but also enables
its domain-expert users to explain it to other stakeholders.

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The categories of this paper are: cond-mat
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The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
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The categories of this paper are: hep-th
9
The categories of this paper are: nlin
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The categories of this paper are: alg-geom
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The categories of this paper are: nucl-ex
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The categories of this paper are: math
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The categories of this paper are: bayes-an
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The categories of this paper are: physics
12
The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
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The categories of this paper are: atom-ph
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The categories of this paper are: q-bio
8
The categories of this paper are: hep-ex