# Addressing Bias in Online Selection with Limited Budget of Comparisons

arXiv: https://arxiv.org/abs/2303.09205 · HTML: https://mlsanity.com/paper/2303.09205/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2023.

## Built on by (most cited first, 3 of 3)

- [Non-clairvoyant Scheduling with Partial Predictions](https://mlsanity.com/paper/2405.01013/) (2024)
- [Learning-Augmented Priority Queues](https://mlsanity.com/paper/2406.04793/) (2024)
- [Time Fairness in Online Knapsack Problems](https://mlsanity.com/paper/2305.13293/) (2023)

## Abstract (arXiv)

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.