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 PRINTED FROM the OXFORD RESEARCH ENCYCLOPEDIA, CRIMINOLOGY AND CRIMINAL JUSTICE ( (c) Oxford University Press USA, 2016. All Rights Reserved. Personal use only; commercial use is strictly prohibited (for details see Privacy Policy and Legal Notice).

date: 25 May 2018

Summary and Keywords

From rudimentary conceptions of risk in the late 18th century, risk assessment slowly evolved toward a more multifaceted conceptualization of risk and progressed to more sophisticated methods to calibrate offender risk levels. This story largely involves the struggles in criminology and applied agencies to achieve a successful “science-to-practice” advancement in risk technologies to support criminal justice decision making. This has involved scientific measurement issues such as reliability, predictive validity, construct validity, and ways to assess the accuracy of predictions and to effectively implement risk assessment methods. The urgent call for higher predictive accuracy from criminal justice policymakers has constantly motivated such change. Over time, the concept of risk has fragmented as diverse agencies, including pretrial release, probation, courts, and jails, have sought to assess specific risk outcomes that are critical for their policy goals. Most agencies are engaged in both risk assessment and risk reduction, with the latter requiring a deeper assessment of explanatory factors. Currently, risk assessment in criminal justice faces several turbulent challenges. The explosive trends in information technology regarding data access, computer memories, and processing speed are combining with new predictive analytic methods that may challenge the currently dominant techniques of risk assessment. A final challenge is that there is, as yet, insufficient standardization of risk assessment methods; nor are there any common language or definitions for offender risk categories. Thus, recent proposals for standardization are examined.

Keywords: risk assessment, predictive accuracy, gender-responsive, risk factors, test bias, machine learning, standardized, terminology, race

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