# Efficient candidate screening under multiple tests and implications for fairness

## TL;DR

This paper extends screening models to multiple noisy tests, analyzing optimal employer policies and revealing fairness challenges when noise varies across groups in candidate evaluation.

## Contribution

It introduces a multi-test screening framework with adaptive policies and highlights inherent fairness limitations across different candidate groups.

## Key findings

- Optimal policies depend on test noise levels and adaptivity.
- Impossibility results show fairness issues when noise differs across groups.
- Multi-test models reveal complex trade-offs in candidate screening.

## Abstract

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.