Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Balancing Competing Objectives with Noisy Data: Score-Based Classifiers
  for Welfare-Aware Machine Learning   While real-world decisions involve many competing objectives, algorithmic
decisions are often evaluated with a single objective function. In this paper,
we study algorithmic policies which explicitly trade off between a private
objective (such as profit) and a public objective (such as social welfare). We
analyze a natural class of policies which trace an empirical Pareto frontier
based on learned scores, and focus on how such decisions can be made in noisy
or data-limited regimes. Our theoretical results characterize the optimal
strategies in this class, bound the Pareto errors due to inaccuracies in the
scores, and show an equivalence between optimal strategies and a rich class of
fairness-constrained profit-maximizing policies. We then present empirical
results in two different contexts -- online content recommendation and
sustainable abalone fisheries -- to underscore the applicability of our
approach to a wide range of practical decisions. Taken together, these results
shed light on inherent trade-offs in using machine learning for decisions that
impact social welfare.

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The categories of this paper are: astro-ph
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The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
2
The categories of this paper are: math
37
The categories of this paper are: supr-con
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The categories of this paper are: nlin
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The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an
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The categories of this paper are: chao-dyn
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The categories of this paper are: plasm-ph
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The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
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The categories of this paper are: eess
31
The categories of this paper are: ao-sci
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The categories of this paper are: patt-sol
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The categories of this paper are: nucl-ex