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Updated: Jun 16, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Published on: January 11, 2020

An Assessment of Racial Disparities in Pretrial Decision-Making Using Misclassification Models.

Kimberly A Hochstedler Webb1,2, Sarah A Riley3, Martin T Wells1

  • 1Department of Statistics and Data Science, Cornell University, Ithaca, New York, USA.

Journal of Empirical Legal Studies
|June 15, 2026
PubMed
Summary
This summary is machine-generated.

Pretrial risk assessment tools show racial bias. The Virginia Pretrial Risk Assessment Instrument (VPRAI) has differing accuracy by race, and judicial decisions result in higher wrongful detention rates for Black defendants.

Keywords:
algorithmic biasbias correctionnoisy labelsracial disparitiesrisk assessmentsensitivityspecificity

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Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
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Last Updated: Jun 16, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

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Published on: January 11, 2020

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

Area of Science:

  • Criminal Justice
  • Data Science
  • Sociology

Background:

  • Pretrial risk assessment tools are widely used to predict failure to appear (FTA) or reoffense.
  • Concerns exist regarding potential bias against minority groups in algorithmic risk assessments and judicial decisions.

Purpose of the Study:

  • To investigate the accuracy and fairness of the Virginia Pretrial Risk Assessment Instrument (VPRAI).
  • To examine racial disparities in algorithmic risk assessments and subsequent judicial decisions.

Main Methods:

  • Developed methods to assess the association between risk factors and pretrial failure.
  • Estimated misclassification rates of risk assessments and judicial decisions based on defendant race.
  • Utilized outcome misclassification methods and simulation studies.

Main Results:

  • The VPRAI algorithm demonstrated near-perfect specificity but varying sensitivity across racial groups.
  • Judicial decisions exhibited racial bias, with higher wrongful detention rates for Black defendants (51.4%) compared to white defendants (39.7%).

Conclusions:

  • Pretrial risk assessment algorithms and judicial decision-making processes show evidence of racial bias.
  • The VPRAI's accuracy varies by race, and judicial decisions lead to disproportionately higher wrongful detentions for Black individuals.