Related Experiment Video
Updated: Jun 16, 2026
12:18
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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
Summary
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.
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.
Related Concept Videos
02:55Stereotypes, Prejudice, and Discrimination
Humans are very diverse and although we share many similarities, we also have many differences. The social groups we belong to help form our identities (Tajfel, 1974). These differences may be difficult for some people to reconcile, which may lead to prejudice toward people who are different. Prejudice is a negative attitude and feeling toward an individual based solely on one’s membership in a particular social group (Allport, 1954; Brown, 2010). Prejudice is common against people who are...
01:25Strategies for Assessing and Addressing Confounding
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
01:27Halo Effect
The halo effect is a cognitive bias in which an individual's overall impression influences judgments about their specific traits. This psychological phenomenon leads people to associate positive characteristics with those they perceive as generally good and negative characteristics with those they view as bad. This effect is particularly influential in social perception, professional evaluations, and decision-making processes.The Psychological Basis of the Halo EffectThe halo effect is rooted...
01:52Regression Toward the Mean
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
02:16Stereotype Content Model
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence categorization, a person will feel...
01:29Bias in Epidemiological Studies
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is: