The Roles of the Risk Estimate and Clinical Information in Risk Assessments

Kroner, Daryl G.

doi:10.1002/9781119139980.ch27

Cited by 1 publication

(1 citation statement)

References 63 publications

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“…268 The global AUC statistic does not distinguish relative ability between true positives (failures correctly classified to fail) and true negatives (nonfailures correctly classified to not fail). 269 Thus, we computed the True Positive Rate (TPR) which indicates the proportion of individuals with new criminal charges correctly predicted to be arrested. 270 The True Negative Rate (TNR) is the proportion of those without new criminal charges who are correctly predicted to succeed.…”

Section: Differential Validitymentioning

confidence: 99%

Investigating Algorithmic Risk and Race

Hamilton¹

2021

UCLA Criminal Justice Law Review

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Risk assessment algorithms lie at the heart of criminal justice reform to tackle mass incarceration. The newest application of risk tools centers on the pretrial stage as a means to reduce both reliance upon wealth-based bail systems and rates of pretrial detention. Yet the ability of risk assessment to achieve the reform movement's goals will be challenged if the risk tools do not perform equitably for minorities. To date, little is known about the racial fairness of these algorithms as they are used in the field. This Article offers an original empirical study of a popular risk assessment tool to evaluate its race-based performance. The case study is novel in employing a two-sample design with large datasets from diverse jurisdictions, one with a supermajority white population and the other a supermajority Black population.Statistical analyses examine whether, in these jurisdictions, the algorithmic risk tool results in disparate impact, exhibits test bias, or displays differential validity in terms of unequal performance metrics for white versus Black defendants. Implications of the study results are informative to the broader knowledge base about risk assessment practices in the field. Results contribute to the debate about the topic of algorithmic fairness in an important setting where one's liberty interests may be infringed despite not being adjudicated guilty of any crime.

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Section: Differential Validitymentioning

confidence: 99%

Investigating Algorithmic Risk and Race

Hamilton¹

2021

UCLA Criminal Justice Law Review

View full text Add to dashboard Cite

show abstract

The Roles of the Risk Estimate and Clinical Information in Risk Assessments

Cited by 1 publication

References 63 publications

Investigating Algorithmic Risk and Race

Investigating Algorithmic Risk and Race

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