A new report from the RAND corporation* describes how background checks can be improved to include information about time spent in the community, thereby more accurately reflecting the risk of recidivism. The authors note that criminal background checks, which are commonly used in the United States, are based on the assumption that past behavior is a good predictor of future behavior. However, their research shows the risk of recidivism declines the longer a person is in the community and does not commit a crime. The authors suggest that any risk-assessment instrument used in background checks should reset the assessment of recidivism risk to the time of the background check and not the time of conviction, in what they call the “reset principle”. They state that “if models based on the reset principle are developed into tools that employers and others can use to assess recidivism risk, they may offer a more accurate way to distinguish candidates’ risk of recidivism. Thus, they may offer many with criminal histories a way to demonstrate that they should be offered another chance.”
Five considerations should guide the development of models that adhere to the reset principle: proper definition of the relevant population, use of conviction data, data sets of a sufficient time span, calibration of estimates, and validation of estimates.
The authors developed a viable recidivism risk-prediction model that adheres to the reset principle based on a large set of criminal justice data from the North Carolina Department of Public Safety.
Observations of the large data set showed that the majority of individuals with a conviction do not have a subsequent conviction.
The North Carolina data showed a person's likelihood of reoffending declines rapidly as more time passes without a conviction. In fact, after a sufficient period without a new conviction, even people initially deemed to be at the highest risk for reoffending (such as those with a more extensive criminal background) transition to risk levels that appear similar to those initially at the lowest risk.
Policymakers should recognize that, over an extended sampling period, most people who get convicted are not reconvicted. This provides a fact base for policymaking that differs from findings by the Bureau of Justice Statistics that articulate that, in a given cohort of people released from prison (e.g., in a given year), most people experience another conviction.
Updates to the Uniform Guidelines on Employee Selection Procedures can validate a new class of models, such as those that satisfy the reset principle, providing employers a more certain defense to challenges to their employment decisions.
Policymakers and other decision-makers should make determinations about risk thresholds that are applied in a particular setting (e.g., an employer deciding how much recidivism risk is appropriate for a given job description) because those thresholds implicate issues of equity and fairness.
Data quality can limit the development of successful recidivism risk models, and policymakers should consider creating data infrastructure that supports models that adhere to the reset principle.
Policymakers should understand that exploring and stressing models that adhere to the reset principle for bias will be crucial. Model predictions may reflect the unfair systemic biases in the current criminal justice system.
Tools that use models that adhere to the reset principle should be developed judiciously and after carefully considering many systemic factors regarding fairness. An adequate assessment of bias should include a comparison to the current state. Even an imperfect tool could provide more opportunities to candidates against whom the current system is biased than the current methods.
*The RAND Corporation is a non-profit, non-partisan research organization that develops “solutions to public policy challenges to help make communities throughout the world safer and more secure, healthier and more prosperous.”
Read the full report at www.rand.org/pubs/research_briefs/RBA1360-1.html