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Building Non-Discriminatory Algorithms in Selected Data
David Arnold
Will Dobbie
Peter Hull
蝌蚪网 Review: Insights (Forthcoming)
Abstract
We develop new quasi-experimental tools to understand algorithmic discrimination and
build non-discriminatory algorithms when the outcome of interest is only selectively observed.
We first show that algorithmic discrimination arises when the available algorithmic
inputs are systematically different for individuals with the same objective potential outcomes.
We then show how algorithmic discrimination can be eliminated by measuring and
purging these conditional input disparities. Leveraging the quasi-random assignment of
bail judges in New York City, we find that our new algorithms not only eliminate algorithmic
discrimination but also generate more accurate predictions by correcting for the
selective observability of misconduct outcomes.
