# Efficient candidate screening under multiple tests and implications for fairness

arXiv: https://arxiv.org/abs/1905.11361 · HTML: https://mlsanity.com/paper/1905.11361/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2019.

## Built on by (most cited first, 16 of 16)

- [Debiasing Methods for Fairer Neural Models in Vision and Language Research: A Survey](https://mlsanity.com/paper/2211.05617/) (2022)
- [Fair Sequential Selection Using Supervised Learning Models](https://mlsanity.com/paper/2110.13986/) (2021)
- [Fairness Aware Counterfactuals for Subgroups](https://mlsanity.com/paper/2306.14978/) (2023)
- [Multi Stage Screening: Enforcing Fairness and Maximizing Efficiency in a Pre-Existing Pipeline](https://mlsanity.com/paper/2203.07513/) (2022)
- [Sequential Strategic Screening](https://mlsanity.com/paper/2301.13397/) (2023)
- [Normalise for Fairness: A Simple Normalisation Technique for Fairness in Regression Machine Learning Problems](https://mlsanity.com/paper/2202.00993/) (2022)
- [FairMILE: Towards an Efficient Framework for Fair Graph Representation Learning](https://mlsanity.com/paper/2211.09925/) (2022)
- [Manifestations of Xenophobia in AI Systems](https://mlsanity.com/paper/2212.07877/) (2022)
- [Fair Secretaries with Unfair Predictions](https://mlsanity.com/paper/2411.09854/) (2024)
- [Fairness in AI and Its Long-Term Implications on Society](https://mlsanity.com/paper/2304.09826/) (2023)
- [Modelling the long-term fairness dynamics of data-driven targeted help on job seekers](https://mlsanity.com/paper/2208.08881/) (2022)
- [Counterfactual Fairness through Transforming Data Orthogonal to Bias](https://mlsanity.com/paper/2403.17852/) (2024)
- [Simultaneous Improvement of ML Model Fairness and Performance by Identifying Bias in Data](https://mlsanity.com/paper/2210.13182/) (2022)
- [FAIRO: Fairness-aware Adaptation in Sequential-Decision Making for Human-in-the-Loop Systems](https://mlsanity.com/paper/2307.05857/) (2023)
- [Fairness on Synthetic Visual and Thermal Mask Images](https://mlsanity.com/paper/2209.08762/) (2022)
- [Analyzing Fairness of Computer Vision and Natural Language Processing Models](https://mlsanity.com/paper/2412.09900/) (2024)

## Abstract (arXiv)

When recruiting job candidates, employers rarely observe their underlying skill level directly. Instead, they must administer a series of interviews and/or collate other noisy signals in order to estimate the worker's skill. Traditional economics papers address screening models where employers access worker skill via a single noisy signal. In this paper, we extend this theoretical analysis to a multi-test setting, considering both Bernoulli and Gaussian models. We analyze the optimal employer policy both when the employer sets a fixed number of tests per candidate and when the employer can set a dynamic policy, assigning further tests adaptively based on results from the previous tests. To start, we characterize the optimal policy when employees constitute a single group, demonstrating some interesting trade-offs. Subsequently, we address the multi-group setting, demonstrating that when the noise levels vary across groups, a fundamental impossibility emerges whereby we cannot administer the same number of tests, subject candidates to the same decision rule, and yet realize the same outcomes in both groups.