Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Causal Perception in Question-Answering Systems   Root cause analysis is a common data analysis task. While question-answering
systems enable people to easily articulate a why question (e.g., why students
in Massachusetts have high ACT Math scores on average) and obtain an answer,
these systems often produce questionable causal claims. To investigate how such
claims might mislead users, we conducted two crowdsourced experiments to study
the impact of showing different information on user perceptions of a
question-answering system. We found that in a system that occasionally provided
unreasonable responses, showing a scatterplot increased the plausibility of
unreasonable causal claims. Also, simply warning participants that correlation
is not causation seemed to lead participants to accept reasonable causal claims
more cautiously. We observed a strong tendency among participants to associate
correlation with causation. Yet, the warning appeared to reduce the tendency.
Grounded in the findings, we propose ways to reduce the illusion of causality
when using question-answering systems.

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The categories of this paper are: nlin
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The categories of this paper are: stat
33
The categories of this paper are: plasm-ph
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The categories of this paper are: cond-mat
20
The categories of this paper are: acc-phys
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The categories of this paper are: q-alg
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The categories of this paper are: solv-int
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The categories of this paper are: q-fin
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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: chao-dyn
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The categories of this paper are: comp-gas
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The categories of this paper are: q-bio
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The categories of this paper are: adap-org
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The categories of this paper are: eess
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The categories of this paper are: bayes-an