Closer than they appear: A Bayesian perspective on individual-level heterogeneity in risk assessment

TL;DR

This paper uses Bayesian hierarchical models to analyze individual risk assessment in the criminal justice system, revealing significant within-group variability and questioning the effectiveness of current risk grouping methods.

Contribution

It introduces a Bayesian approach to quantify individual-level heterogeneity in risk assessment, highlighting limitations of group-based risk stratification.

Findings

Large individual variability within risk groups

Uncertainty in individual risk estimates is high

Current risk groups do not meaningfully distinguish individuals

Abstract

Risk assessment instruments are used across the criminal justice system to estimate the probability of some future behavior given covariates. The estimated probabilities are then used in making decisions at the individual level. In the past, there has been controversy about whether the probabilities derived from group-level calculations can meaningfully be applied to individuals. Using Bayesian hierarchical models applied to a large longitudinal dataset from the court system in the state of Kentucky, we analyze variation in individual-level probabilities of failing to appear for court and the extent to which it is captured by covariates. We find that individuals within the same risk group vary widely in their probability of the outcome. In practice, this means that allocating individuals to risk groups based on standard approaches to risk assessment, in large part, results in creating distinctions among individuals who are not meaningfully different in terms of their likelihood of the outcome. This is because uncertainty about the probability that any particular individual will fail to appear is large relative to the difference in average probabilities among any reasonable set of risk groups.