Addressing Bias in Online Selection with Limited Budget of Comparisons

Abstract

Consider a hiring process with candidates coming from different universities. It is easy to order candidates with the same background, yet it can be challenging to compare them otherwise. The latter case requires additional costly assessments, leading to a potentially high total cost for the hiring organization. Given an assigned budget, what would be an optimal strategy to select the most qualified candidate?We model the above problem as a multicolor secretary problem, allowing comparisons between candidates from distinct groups at a fixed cost. Our study explores how the allocated budget enhances the success probability of online selection algorithms.

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Cite

Text

Benomar et al. "Addressing Bias in Online Selection with Limited Budget of Comparisons." Neural Information Processing Systems, 2024. doi:10.52202/079017-0361

Markdown

[Benomar et al. "Addressing Bias in Online Selection with Limited Budget of Comparisons." Neural Information Processing Systems, 2024.](https://mlanthology.org/neurips/2024/benomar2024neurips-addressing/) doi:10.52202/079017-0361

BibTeX

@inproceedings{benomar2024neurips-addressing,
  title     = {{Addressing Bias in Online Selection with Limited Budget of Comparisons}},
  author    = {Benomar, Ziyad and Chzhen, Evgenii and Schreuder, Nicolas and Perchet, Vianney},
  booktitle = {Neural Information Processing Systems},
  year      = {2024},
  doi       = {10.52202/079017-0361},
  url       = {https://mlanthology.org/neurips/2024/benomar2024neurips-addressing/}
}