Efficient candidate screening under multiple tests and implications for fairness

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,
% in which tests are
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.

• Algorithms and Theory

• Machine Intelligence