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Research Questions

  1. How can employment background checks account for the time a person spends free in the community after their last interaction with the criminal justice system?
  2. Is it possible to create a viable risk-prediction model based on the reset principle?
  3. What considerations should guide the creation of models that adhere to the reset principle?
  4. What do observations made on a large data set from the North Carolina Department of Public Safety indicate about the frequency with which people who have a conviction are reconvicted, how quickly their likelihood of reoffending declines, and how their risk profiles change?

Criminal background checks are commonly used in the United States to screen people for various opportunities, including employment. The evaluations operate on a broadly accepted assumption that past behavior is a good predictor of future behavior.

Contrary to popular belief, however, most who enter the criminal justice system ultimately desist from crime. The risk of recidivism declines the longer a person is in the community and does not commit a crime. Eventually, a past criminal record is no longer predictive of future convictions.

However, most background checks do not adequately account for the time someone spends in the community without a new conviction. Not having updated information may skew the risk assessment to make people look riskier than they are, resulting in denied opportunities.

The authors describe how background checks can be improved to include information about time spent in the community, thereby more accurately reflecting risk of recidivism. They suggest that any viable 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, as current methods derived from the criminal justice context do. The authors call this the reset principle. 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.

Key Findings

  • The reset principle states that any viable 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, as current methods derived from the criminal justice context do.
  • 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.
  • Five considerations should guide 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.
  • 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.
  • The North Carolina data showed that, after a sufficient period without a new conviction, even people initially deemed to be at 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.

Recommendations

  • 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 decisionmakers 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.

Table of Contents

  • Chapter One

    Introduction

  • Chapter Two

    Legal Context for Criminal History Checks at Hiring

  • Chapter Three

    The Reset Principle for Background Checks

  • Chapter Four

    Considerations for Building Recidivism Prediction Models That Follow the Reset Principle

  • Chapter Five

    A Risk-Prediction Model That Follows the Reset Principle

  • Chapter Six

    Workshop Proceedings

  • Chapter Seven

    Providing Another Chance: The Reset Principle's Implications for Policy

  • Appendix A

    Additional Data Considerations

  • Appendix B

    More on Survival Analysis

  • Appendix C

    Description of Data Used for Modeling

Research conducted by

This research was sponsored by Arnold Ventures and conducted by the Justice Policy Program within RAND Social and Economic Well-Being.

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