Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Appropriate Fairness Perceptions? On the Effectiveness of Explanations
  in Enabling People to Assess the Fairness of Automated Decision Systems   It is often argued that one goal of explaining automated decision systems
(ADS) is to facilitate positive perceptions (e.g., fairness or trustworthiness)
of users towards such systems. This viewpoint, however, makes the implicit
assumption that a given ADS is fair and trustworthy, to begin with. If the ADS
issues unfair outcomes, then one might expect that explanations regarding the
system's workings will reveal its shortcomings and, hence, lead to a decrease
in fairness perceptions. Consequently, we suggest that it is more meaningful to
evaluate explanations against their effectiveness in enabling people to
appropriately assess the quality (e.g., fairness) of an associated ADS. We
argue that for an effective explanation, perceptions of fairness should
increase if and only if the underlying ADS is fair. In this in-progress work,
we introduce the desideratum of appropriate fairness perceptions, propose a
novel study design for evaluating it, and outline next steps towards a
comprehensive experiment.

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The categories of this paper are: plasm-ph
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The categories of this paper are: stat
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The categories of this paper are: q-alg
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The categories of this paper are: acc-phys
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-th
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The categories of this paper are: bayes-an
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The categories of this paper are: patt-sol
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The categories of this paper are: math-ph
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The categories of this paper are: solv-int
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The categories of this paper are: q-bio
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The categories of this paper are: mtrl-th
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The categories of this paper are: supr-con
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The categories of this paper are: atom-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: nucl-th