Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Empirical Study of Accuracy, Fairness, Explainability, Distributional
  Robustness, and Adversarial Robustness   To ensure trust in AI models, it is becoming increasingly apparent that
evaluation of models must be extended beyond traditional performance metrics,
like accuracy, to other dimensions, such as fairness, explainability,
adversarial robustness, and distribution shift. We describe an empirical study
to evaluate multiple model types on various metrics along these dimensions on
several datasets. Our results show that no particular model type performs well
on all dimensions, and demonstrate the kinds of trade-offs involved in
selecting models evaluated along multiple dimensions.

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The categories of this paper are: q-bio
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The categories of this paper are: econ
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The categories of this paper are: math-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: comp-gas
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The categories of this paper are: dg-ga
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The categories of this paper are: quant-ph
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The categories of this paper are: stat
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The categories of this paper are: chem-ph
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The categories of this paper are: eess
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The categories of this paper are: plasm-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-lat
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The categories of this paper are: hep-th
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The categories of this paper are: mtrl-th
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The categories of this paper are: nlin