Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Making the Invisible Visible: Risks and Benefits of Disclosing Metadata
  in Visualization   Accompanying a data visualization with metadata may benefit readers by
facilitating content understanding, strengthening trust, and providing
accountability. However, providing this kind of information may also have
negative, unintended consequences, such as biasing readers' interpretations, a
loss of trust as a result of too much transparency, and the possibility of
opening visualization creators with minoritized identities up to undeserved
critique. To help future visualization researchers and practitioners decide
what kinds of metadata to include, we discuss some of the potential benefits
and risks of disclosing five kinds of metadata: metadata about the source of
the underlying data; the cleaning and processing conducted; the marks,
channels, and other design elements used; the people who directly created the
visualization; and the people for whom the visualization was created. We
conclude by proposing a few open research questions related to how to
communicate metadata about visualizations.

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The categories of this paper are: bayes-an
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The categories of this paper are: q-alg
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The categories of this paper are: math-ph
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The categories of this paper are: funct-an
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The categories of this paper are: nlin
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The categories of this paper are: q-fin
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-lat
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The categories of this paper are: alg-geom
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The categories of this paper are: econ
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The categories of this paper are: mtrl-th
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The categories of this paper are: eess
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The categories of this paper are: hep-ex
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The categories of this paper are: chao-dyn
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The categories of this paper are: chem-ph
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The categories of this paper are: solv-int