• Corpus ID: 221507834

Fair and Useful Cohort Selection

@article{SmedemarkMargulies2020FairAU,
  title={Fair and Useful Cohort Selection},
  author={Niklas Smedemark-Margulies and Paul Langton and Huy L. Nguyen},
  journal={ArXiv},
  year={2020},
  volume={abs/2009.02207}
}
As important decisions about the distribution of society's resources become increasingly automated, it is essential to consider the measurement and enforcement of fairness in these decisions. In this work we build on the results of Dwork and Ilvento ITCS'19, which laid the foundations for the study of fair algorithms under composition. In particular, we study the cohort selection problem, where we wish to use a fair classifier to select $k$ candidates from an arbitrarily ordered set of size $n… 
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Tables from this paper

Fair and Optimal Cohort Selection for Linear Utilities
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This work introduces a specific instance of cohort selection where the goal is to choose a cohort maximizing a linear utility function and gives approximately optimal polynomial-time algorithms for this problem in both an offline setting where the entire fair classifier is given at once, or an online setting where candidates arrive one at a time and are classified as they arrive.

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This work introduces a specific instance of cohort selection where the goal is to choose a cohort maximizing a linear utility function and gives approximately optimal polynomial-time algorithms for this problem in both an offline setting where the entire fair classifier is given at once, or an online setting where candidates arrive one at a time and are classified as they arrive.
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