Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data
  and Bayesian Inference   We investigate the problem of reliably assessing group fairness when labeled
examples are few but unlabeled examples are plentiful. We propose a general
Bayesian framework that can augment labeled data with unlabeled data to produce
more accurate and lower-variance estimates compared to methods based on labeled
data alone. Our approach estimates calibrated scores for unlabeled examples in
each group using a hierarchical latent variable model conditioned on labeled
examples. This in turn allows for inference of posterior distributions with
associated notions of uncertainty for a variety of group fairness metrics. We
demonstrate that our approach leads to significant and consistent reductions in
estimation error across multiple well-known fairness datasets, sensitive
attributes, and predictive models. The results show the benefits of using both
unlabeled data and Bayesian inference in terms of assessing whether a
prediction model is fair or not.

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The categories of this paper are: chem-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: ao-sci
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The categories of this paper are: nucl-ex
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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: econ
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The categories of this paper are: hep-lat
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The categories of this paper are: q-alg
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The categories of this paper are: cond-mat
5
The categories of this paper are: gr-qc
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The categories of this paper are: comp-gas
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The categories of this paper are: patt-sol
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The categories of this paper are: mtrl-th
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The categories of this paper are: physics
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The categories of this paper are: quant-ph