Racist Algorithms or Systemic Problems? Risk Assessments and Racial Disparities

As recent and historical events attest, racial and ethnic disparities are widely engrained into the justice system. Recently, scholars and policymakers have raised concerns that risk assessment instruments may exacerbate these disparities. While it is critical that risk instruments be scrutinized for racial bias, some concerns, though well-meaning, have gone beyond the evidence. This article explains what it means for an instrument to be “biased” and why instruments should not all be painted with the same brush (some will be more susceptible to bias than others). If some groups get apprehended more, those groups will score higher on non-biased, well-validated instruments derived to maximize prediction of recidivism because of mathematics. Thus, risk instruments shine a light on long-standing systemic problems of racial disparities. This article concludes with suggestions for research and for minimizing disparities by ensuring that systems use risk assessments to avoid unnecessary incarceration while allowing for structured discretion.

A More Consistent Application of the RNR Model

The development of risk–needs assessments has substantially expanded assessment content, which is reflected in the now regular use of both static and dynamic items. However, while the risk–need–responsivity model differentiates between risks and needs theoretically, the scoring of risks and needs does not make for a clear demarcation. We argue that an assessment of an offender’s needs should be scored separately and solely on items that are changeable and predict recidivism. This article describes the conceptualization and development of Washington State’s offender needs assessment. Designed to complement an offender’s assessment of risk, we make use of key design elements to avoid many theoretical and methodological caveats. Using preexisting item selection, weighting, and validation methods, we present domain-based needs models that maximize item content and provide substantial performance in the prediction of recidivism.