Effects of pretrial risk assessments on release decisions and misconduct outcomes relative to practice as usual

Lowder, Evan M.; Diaz, Carmen L.; Grommon, Eric; Ray, Bradley

doi:10.1016/j.jcrimjus.2020.101754

Cited by 14 publications

(28 citation statements)

References 28 publications

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“…Total scores ranging from 0 to 9 classify individuals into three risk categories: Low (0-2), Moderate (3-5), and High (6+). IRAS-PAT assessments have produced predictive accuracy similar to other pretrial risk assessments (Desmarais et al, 2020;Lowder et al, 2020b). Supervision level (Low, Moderate, High) measured the judge-ordered supervision level.…”

Section: Independent Variables Independent Variables Included Iras-pat Risk Levelmentioning

confidence: 76%

Use of Risk Assessments in Pretrial Supervision Decision-Making and Associated Outcomes

Lowder

Foudray

2021

Crime & Delinquency

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Despite the growing use of both pretrial risk assessment and supervision as pretrial reform strategies, there has been limited investigation on the effectiveness of risk assessment-informed pretrial supervision. We conducted a multi-site, retrospective investigation in 1,505 pretrial defendants from four local jurisdictions to examine risk assessment-informed pretrial supervision decisions and associated pretrial misconduct outcomes. Our findings showed pretrial supervision decisions were generally consistent with structured guidelines and defendant risk classifications. Use of bond and electronic monitoring had little impact on pretrial misconduct. However, more frequent pretrial monitoring was associated with higher rates of pretrial misconduct across all risk levels. Reducing supervision conditions and monitoring for low-risk defendants, in particular, may help reduce rates of misconduct in pretrial populations.

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Section: Independent Variables Independent Variables Included Iras-pat Risk Levelmentioning

confidence: 76%

Use of Risk Assessments in Pretrial Supervision Decision-Making and Associated Outcomes

Lowder

Foudray

2021

Crime & Delinquency

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“…Thus, it is difficult to appraise generalizability of these release rates across pretrial settings. In pretrial settings more broadly, anywhere between 50 (Dobbie & Yang, 2019) to near 90% (Lowder et al, 2021) of defendants are released pretrial. This variability in release rates has meaningful implications for predictive validity estimates in jurisdictions where release rates are close to 50% or below for moderate- and high-risk defendants.…”

Section: Discussionmentioning

confidence: 99%

Pretrial risk assessment validation research: Range restriction and attenuation of predictive validity estimates.

Lowder¹,

Wilson²

2021

Law and Human Behavior

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Objectives: A growing number of studies have examined the predictive validity of pretrial risk assessments. Overwhelmingly, these validation studies are conducted in the context of routine practice, where not all individuals who are assessed receive pretrial release. Despite evidence of range restriction in pretrial validation research, no study to date has corrected for range restriction in predictive validity estimates. To address this limitation, we examined the effects of range restriction on predictive validity estimates under varying conditions. Method: We ran simulations based on data from a local validation of 1,030 pretrial defendants to demonstrate the effects of range restriction on predictive validity estimates under different degrees of range restriction, various population correlations, and with and without the influence of a third variable, z, representing discretionary decision making. We examined the effect of range restriction on correlation coefficient (r) and nonparametric Area Under the Curve (AUC) statistics. Results: Under a realistic population correlation (q = .40), attenuation of r ranged from 1-29% for total scores and 8-39% for risk levels across conditions. Under similar conditions, attenuation for AUC estimates ranged from 1-13% for total scores and 1-20% for risk levels. Attenuation was greater with the influence of a secondary selection variable modeling discretionary decision making above and beyond the risk assessment tool. Conclusion: Range restriction may meaningfully reduce predictive validity estimates when greater than 40% of moderate risk and 60% of high risk defendants are detained. Public Significance StatementIncreasingly, jurisdictions are looking to validate pretrial risk assessments used in practice. Risk assessments may be less accurate in predicting outcomes when a substantial proportion of moderateand high-risk defendants are detained pending trial. Future studies should report on the proportion of assessed defendants detained pretrial and correct findings for potential loss of observations.

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“…Differences across subgroups like those found in this study likely reflect system-level disparities (e.g., Kochel et al, 2011). To truly determine whether algorithmic pretrial risk assessments affect disparities in pretrial decisions and outcomes, researchers and other stakeholders must examine these outcomes when assessments are used compared to practice as usual (Lowder et al, 2021; Skeem & Lowenkamp, 2020; Vincent & Viljoen, 2020). Finally, our discussion has focused on the implications that dichotomizing risk scores has for estimating risk, but it is also true that these dichotomies impede risk management.…”

Section: Discussionmentioning

confidence: 99%

Evaluating Fairness of Algorithmic Risk Assessment Instruments: The Problem With Forcing Dichotomies

Zottola¹,

Desmarais

Lowder

et al. 2021

Criminal Justice and Behavior

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Researchers and stakeholders have developed many definitions to evaluate whether algorithmic pretrial risk assessment instruments are fair in terms of their error and accuracy. Error and accuracy are often operationalized using three sets of indicators: false-positive and false-negative percentages, false-positive and false-negative rates, and positive and negative predictive value. To calculate these indicators, a threshold must be set, and continuous risk scores must be dichotomized. We provide a data-driven examination of these three sets of indicators using data from three studies on the most widely used algorithmic pretrial risk assessment instruments: the Public Safety Assessment, the Virginia Pretrial Risk Assessment Instrument, and the Federal Pretrial Risk Assessment. Overall, our findings highlight how conclusions regarding fairness are affected by the limitations of these indicators. Future work should move toward examining whether there are biases in how the risk assessment scores are used to inform decision-making.

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Effects of pretrial risk assessments on release decisions and misconduct outcomes relative to practice as usual

Cited by 14 publications

References 28 publications

Use of Risk Assessments in Pretrial Supervision Decision-Making and Associated Outcomes

Use of Risk Assessments in Pretrial Supervision Decision-Making and Associated Outcomes

Pretrial risk assessment validation research: Range restriction and attenuation of predictive validity estimates.

Evaluating Fairness of Algorithmic Risk Assessment Instruments: The Problem With Forcing Dichotomies

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