A Bayesian Model of Cash Bail Decisions
Joshua Williams, J. Zico Kolter

TL;DR
This paper introduces a hierarchical Bayesian model to analyze cash bail decisions, revealing racial disparities in bail assignments even when controlling for court appearance likelihoods.
Contribution
It develops a probabilistic model that estimates judge decision-making costs and uncovers racial bias in bail decisions across multiple judges.
Findings
Black defendants are more likely to be assigned cash bail than white defendants with similar court skipping probabilities.
The model quantifies judge-specific decision costs and disparities.
Analysis of 50,000 cases shows systemic racial bias in bail decisions.
Abstract
The use of cash bail as a mechanism for detaining defendants pre-trial is an often-criticized system that many have argued violates the presumption of "innocent until proven guilty." Many studies have sought to understand both the long-term effects of cash bail's use and the disparate rate of cash bail assignments along demographic lines (race, gender, etc). However, such work is often susceptible to problems of infra-marginality -- that the data we observe can only describe average outcomes, and not the outcomes associated with the marginal decision. In this work, we address this problem by creating a hierarchical Bayesian model of cash bail assignments. Specifically, our approach models cash bail decisions as a probabilistic process whereby judges balance the relative costs of assigning cash bail with the cost of defendants potentially skipping court dates, and where these skip…
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