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Judicial officers, in turn, use these assessments to determine whether to release or detain defendants during trial. While algorithmic risk assessment tools were designed to predict pretrial failure with greater accuracy relative to judges, there is still concern that both risk assessment recommendations and pretrial decisions are biased against minority groups. We use the Virginia Pretrial Risk Assessment Instrument (VPRAI) as a case study to investigate the accuracy and fairness of risk assessment algorithms and judicial decisions. In this paper, we develop methods to investigate the association between risk factors and pretrial failure, while simultaneously estimating misclassification rates of pretrial risk assessments and of judicial decisions as a function of defendant race. This approach adds to a growing literature that makes use of outcome misclassification methods to answer questions about fairness in pretrial decision\u2010making. We give a detailed simulation study for our proposed methodology and apply these methods to data from the Virginia Department of Criminal Justice Services. We estimate that the VPRAI algorithm has near\u2010perfect specificity, but its sensitivity differs by defendant race. Judicial decisions also display evidence of bias; we estimate wrongful detention rates of 39.7% and 51.4% among white and Black defendants, respectively.<\/jats:p>","DOI":"10.1111\/jels.70031","type":"journal-article","created":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T09:05:03Z","timestamp":1776330303000},"page":"246-269","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An Assessment of Racial Disparities in Pretrial Decision\u2010Making Using Misclassification Models"],"prefix":"10.1111","volume":"23","author":[{"given":"Kimberly A.","family":"Hochstedler Webb","sequence":"first","affiliation":[{"name":"Department of Statistics and Data Science Cornell University  Ithaca New York USA"},{"name":"Division of General Internal Medicine University of Pittsburgh  Pittsburgh Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sarah A.","family":"Riley","sequence":"additional","affiliation":[{"name":"Vera Institute of Justice Incarceration and Inequality Project  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin T.","family":"Wells","sequence":"additional","affiliation":[{"name":"Department of Statistics and Data Science Cornell University  Ithaca New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,4,16]]},"reference":[{"key":"e_1_2_11_2_1","unstructured":"Angwin J. andJ.Larson.2016.\u201cMachine Bias.\u201dAccessed 16 February 2023.https:\/\/www.propublica.org\/article\/machine\u2010bias\u2010risk\u2010assessments\u2010in\u2010criminal\u2010sentencing."},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.3386\/w26999"},{"key":"e_1_2_11_4_1","first-page":"246","article-title":"Algorithms in Judges' Hands: Incarceration and Inequity in Broward County, Florida","volume":"7","author":"Bahl U.","year":"2023","journal-title":"UCLA Law Review Discourse"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1080\/10530789.2019.1622858"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1111\/biom.13400"},{"key":"e_1_2_11_7_1","first-page":"685","article-title":"The Bail Reform Act of 1984","volume":"34","author":"Berg K. 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M.Spruance.2016.\u201cRace and Gender Neutral Pretrial Risk Assessment Release Recommendations and Supervision: VPRAI and Praxis Revised.\u201d"},{"key":"e_1_2_11_20_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1977.tb01600.x"},{"key":"e_1_2_11_21_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1745-9125.2003.tb01007.x"},{"key":"e_1_2_11_22_1","doi-asserted-by":"publisher","DOI":"10.1525\/sp.2004.51.2.222"},{"key":"e_1_2_11_23_1","unstructured":"Desmarais S. andJ.Singh.2013.\u201cRisk Assessment Instruments Validated and Implemented in Correctional Settings in the United States.\u201d"},{"key":"e_1_2_11_24_1","doi-asserted-by":"publisher","DOI":"10.1257\/aer.20161503"},{"key":"e_1_2_11_25_1","volume-title":"Automating Inequality: How High\u2010Tech Tools Profile, Police, and Punish the Poor","author":"Eubanks V.","year":"2018"},{"key":"e_1_2_11_26_1","first-page":"2325","volume-title":"International Conference on Artificial Intelligence and Statistics","author":"Fogliato R.","year":"2020"},{"key":"e_1_2_11_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF01039804"},{"key":"e_1_2_11_28_1","doi-asserted-by":"publisher","DOI":"10.1111\/jels.12343"},{"key":"e_1_2_11_29_1","doi-asserted-by":"publisher","DOI":"10.2307\/799695"},{"key":"e_1_2_11_30_1","doi-asserted-by":"publisher","DOI":"10.1093\/jrsssa\/qnad010"},{"key":"e_1_2_11_31_1","unstructured":"Jung J. 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