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Fast Threshold Tests for Detecting Discrimination | | Emma Pierson
; Sam Corbett-Davies
; Sharad Goel
; | | Date: |
27 Feb 2017 | | Abstract: | Threshold tests have recently been proposed as a robust 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
Bayesian latent variable model for which inference is often computationally
challenging. Here we develop a method for fitting threshold tests that is more
than 75 times faster than the existing approach, reducing computation from
hours to minutes. We demonstrate this technique by analyzing 2.7 million police
stops of pedestrians in New York City between 2008 and 2012. 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. These
discriminant distributions may aid inference in a variety of applications
beyond threshold tests. | | Source: | arXiv, 1702.8536 | | Services: | Forum | Review | PDF | Favorites | |
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