Fast Threshold Tests for Detecting Discrimination
This work addresses a computational bottleneck for practitioners using threshold tests in domains like lending, hiring, and policing, though it is incremental as it focuses on speed improvements rather than new detection capabilities.
The paper tackled the computational challenge of fitting threshold tests for detecting discrimination by developing a method that is two orders of magnitude faster, reducing computation from hours to minutes, and demonstrated it on 2.7 million police stops in New York City.
Threshold tests have recently been proposed as a useful method for detecting bias in lending, hiring, and policing decisions. For example, in the case of credit extensions, these tests aim to estimate the bar for granting loans to white and minority applicants, with a higher inferred threshold for minorities indicative of discrimination. This technique, however, requires fitting a complex Bayesian latent variable model for which inference is often computationally challenging. Here we develop a method for fitting threshold tests that is two orders of magnitude faster than the existing approach, reducing computation from hours to minutes. To achieve these performance gains, we introduce and analyze a flexible family of probability distributions on the interval [0, 1] -- which we call discriminant distributions -- that is computationally efficient to work with. We demonstrate our technique by analyzing 2.7 million police stops of pedestrians in New York City.