Efficient candidate screening under multiple tests and implications for fairness

By Lee Cohen, Zachary Chase Lipton, Yishay Mansour. 2019

36 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Management Science and Operations Research, Statistics and Probability, AI Ethics, Fairness & Society

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Citations in the corpus

A Survey on Bias and Fairness in Machine Learning (2019, background): “etrated every aspect of our daily life. Algorithms make movie recommendations, suggest products to buy, and are increasingly used in high-stakes decisions in loan applications [93], dating and hiring [18, 35]. There are clear benefits to algorithmic decision-making; unlike people, machines do not be”
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects (2020, background): “…above are prevalent not only in finance [20, 133] but also in justice (e.g., pretrial bail) [10], healthcare (e.g., prescription of life-altering medication) [24, 27, 72], and other settings (e.g., hiring) [43, 134, 160] broadly classified as consequential decision-making settings [20, 34, 45, 91].”