Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Uncertainty Estimation and Out-of-Distribution Detection for
  Counterfactual Explanations: Pitfalls and Solutions   Whilst an abundance of techniques have recently been proposed to generate
counterfactual explanations for the predictions of opaque black-box systems,
markedly less attention has been paid to exploring the uncertainty of these
generated explanations. This becomes a critical issue in high-stakes scenarios,
where uncertain and misleading explanations could have dire consequences (e.g.,
medical diagnosis and treatment planning). Moreover, it is often difficult to
determine if the generated explanations are well grounded in the training data
and sensitive to distributional shifts. This paper proposes several practical
solutions that can be leveraged to solve these problems by establishing novel
connections with other research works in explainability (e.g., trust scores)
and uncertainty estimation (e.g., Monte Carlo Dropout). Two experiments
demonstrate the utility of our proposed solutions.

3
The categories of this paper are: physics
18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
7
The categories of this paper are: hep-th
12
The categories of this paper are: nucl-th
2
The categories of this paper are: math
16
The categories of this paper are: stat
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
19
The categories of this paper are: eess
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The categories of this paper are: comp-gas
10
The categories of this paper are: q-bio
15
The categories of this paper are: nucl-ex
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The categories of this paper are: cmp-lg
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The categories of this paper are: ao-sci
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The categories of this paper are: nlin