在有限比较预算下解决在线选择中的偏差问题
Addressing Bias in Online Selection with Limited Budget of Comparisons
AI总结：
本文将跨背景候选人招聘建模为多色秘书问题，允许以固定成本比较不同群体候选人，研究有限比较预算如何提升在线选择算法的成功概率。
摘要：
考虑一个候选人来自不同大学的招聘过程。对相同背景的候选人进行排序很容易，但跨背景比较却颇具挑战。后者需要额外的高成本评估，导致招聘组织面临高昂的总成本。在给定预算下，选择最具资格候选人的最优策略是什么？我们将上述问题建模为多色秘书问题，允许以固定成本比较不同群体的候选人。我们的研究探讨了分配的预算如何提升在线选择算法的成功概率。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
