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The principal concern is that any benefits achieved by safely reducing rates of incarceration will be offset by costs to racial justice claimed to be inherent in the algorithms themselves. But fairness trade\u2010offs are inherent to the task of predicting recidivism, whether the prediction is made by an algorithm or human. Based on a matched sample of 67,784 Black and White federal supervisees assessed with the Post Conviction Risk Assessment, we compared how three alternative strategies for \u201cdebiasing\u201d algorithms affect these trade\u2010offs, using arrest for a violent crime as the criterion . These candidate algorithms all strongly predict violent reoffending (areas under the curve = 0.71\u201372), but vary in their association with race (<jats:italic>r =<\/jats:italic> 0.00\u20130.21) and shift trade\u2010offs between balance in positive predictive value and false\u2010positive rates. Providing algorithms with access to race (rather than omitting race or \u201cblinding\u201d its effects) can maximize calibration and minimize imbalanced error rates. Implications for policymakers with value preferences for efficiency versus equity are discussed.<\/jats:p>","DOI":"10.1002\/bsl.2465","type":"journal-article","created":{"date-parts":[[2020,5,5]],"date-time":"2020-05-05T13:15:17Z","timestamp":1588684517000},"page":"259-278","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["Using algorithms to address trade\u2010offs inherent in predicting recidivism"],"prefix":"10.1002","volume":"38","author":[{"given":"Jennifer","family":"Skeem","sequence":"first","affiliation":[{"name":"Schools of Social Welfare and Public Policy University of California  Berkeley CA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher","family":"Lowenkamp","sequence":"additional","affiliation":[{"name":"Administrative Office, U.S. Courts, Washington DC, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2020,5,5]]},"reference":[{"key":"e_1_2_5_2_1","volume-title":"The standards for educational and psychological testing","author":"American Educational Research Association, American Psychological Association, and National Council on Measurement in Education","year":"2014"},{"key":"e_1_2_5_3_1","unstructured":"Angwin J. 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