On the Validity of Arrest as a Proxy for Offense: Race and the Likelihood of Arrest for Violent Crimes

TL;DR

This study examines racial disparities in arrest rates for violent crimes across US states, highlighting how biases and geographic variations impact the validity of arrest data as a proxy for actual offending behavior.

Contribution

The paper analyzes racial disparities in violent arrest data and discusses implications for the use of arrest-based risk assessments in criminal justice.

Findings

Racial disparities in arrest likelihood vary by crime characteristics.

Significant geographic variation exists in arrest rates.

Discrepancies between re-arrest and re-offense challenge the validity of arrest data.

Abstract

The risk of re-offense is considered in decision-making at many stages of the criminal justice system, from pre-trial, to sentencing, to parole. To aid decision makers in their assessments, institutions increasingly rely on algorithmic risk assessment instruments (RAIs). These tools assess the likelihood that an individual will be arrested for a new criminal offense within some time window following their release. However, since not all crimes result in arrest, RAIs do not directly assess the risk of re-offense. Furthermore, disparities in the likelihood of arrest can potentially lead to biases in the resulting risk scores. Several recent validations of RAIs have therefore focused on arrests for violent offenses, which are viewed as being more accurate reflections of offending behavior. In this paper, we investigate biases in violent arrest data by analysing racial disparities in the likelihood of arrest for White and Black violent offenders. We focus our study on 2007--2016 incident-level data of violent offenses from 16 US states as recorded in the National Incident Based Reporting System (NIBRS). Our analysis shows that the magnitude and direction of the racial disparities depend on various characteristics of the crimes. In addition, our investigation reveals large variations in arrest rates across geographical locations and offense types. We discuss the implications of the observed disconnect between re-arrest and re-offense in the context of RAIs and the challenges around the use of data from NIBRS to correct for the sampling bias.