Position: PhD Student
Current Institution: Stanford University
Abstract: Fast Threshold Tests for Detecting Discrimination
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.
Emma is a fourth-year PhD student in the Leskovec lab in the Stanford Department of Computer Science. Previously, she completed an master’s degree in statistics at Oxford University on a Rhodes Scholarship. Her research applies statistics and machine learning to two application areas: (1) computational health and (2) discrimination and inequality. She also writes about these topics for broader audiences in publications including The New York Times, The Washington Post, The Atlantic, FiveThirtyEight, and Wired. Her work received a best paper award (AISTATS 2018), a best poster award (ICML Workshop on Computational Biology 2016), and a best talk award (HitSeq 2015 at ISMB). She is supported by Hertz and NDSEG Fellowships.