Addressing Bias in Online Selection with Limited Budget of Comparisons
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2023
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866915023370059776 |
|---|---|
| author | Benomar, Ziyad Chzhen, Evgenii Schreuder, Nicolas Perchet, Vianney |
| author_facet | Benomar, Ziyad Chzhen, Evgenii Schreuder, Nicolas Perchet, Vianney |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_09205 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Addressing Bias in Online Selection with Limited Budget of Comparisons Benomar, Ziyad Chzhen, Evgenii Schreuder, Nicolas Perchet, Vianney Computer Science and Game Theory Data Structures and Algorithms 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. |
| title | Addressing Bias in Online Selection with Limited Budget of Comparisons |
| topic | Computer Science and Game Theory Data Structures and Algorithms |
| url | https://arxiv.org/abs/2303.09205 |