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California Static Risk Assessment (CSRA) Susan Turner, UCI October 16, 2008.

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Presentation on theme: "California Static Risk Assessment (CSRA) Susan Turner, UCI October 16, 2008."— Presentation transcript:

1 California Static Risk Assessment (CSRA) Susan Turner, UCI October 16, 2008

2 CDCR Has Embraced Risk Based Decision-Making Recommended by reviewers of CDCR practices –Expert panel recommendations Risk assessment part of the California Logic Model –Strike Team Realigning parole resources with offender risk Actuarial risk tools are used in criminal justice for a variety of purposes –Pretrial release, sentencing guidelines, parole release and revocation decisions, probation caseload assignment, priority for programming

3 How Actuarial Risk Prediction Works—Auto Insurance Example Insurers want to know the likelihood that a driver will be in an accident They use their extensive records data to determine what factors are related to drivers experiencing an accident The model: Risk (accident)=age + gender + zip code + driving experience +…etc.

4 States are Currently Using Risk Assessment Tools in Parole Pennsylvania uses LSI and Static 99 along with violence indicator, institutional programming and behaviorPennsylvania uses LSI and Static 99 along with violence indicator, institutional programming and behavior Maryland uses crime and 6-item risk assessment for parole eligibilityMaryland uses crime and 6-item risk assessment for parole eligibility

5 Actuarial Risk Prediction Has Benefits and Drawbacks Strengths –Promotes efficiency, consistency and objectivity in decision-making –Has an empirical basis –Is more accurate than clinical judgment Limitations –Decisions are based on aggregate, or group, performance “Good” people with “bad” characteristics penalized “Bad” people with “good” characteristics get a break

6 A Number of Outcomes Can Be Predicted in Corrections Most common is recidivism over a certain time period (e.g. three years) Other outcomes may be important for program evaluation –Drug use, employment, mental health status, etc. Availability/ease can drive this choice –Return to Custody (CDCR data) –Arrest (DoJ data) –Conviction (DoJ data)

7 Each Recidivism Outcome Offers Something Different Arrest –Captures the most criminal behavior –Most likely to “over-capture” Conviction –Highest standard of proof –Many instances of criminal behavior do not result in conviction for a new offense Return to Custody –Most direct impact on institutions population CDCR has elected to use arrest as the outcome Most conservative outcome for public safety protection

8 UCI Asked to Assist with Risk Prediction for the CDCR Population Develop an actuarial risk prediction instrument using available data Validate the instrument to determine predictive power for the CDCR population Tool will operate as a “plug-in” to the existing COMPAS system

9 UCI Drew on Washington State Work to Develop CSRA Washington State Institute for Public Policy (WSIPP) started by testing the items on the LSI-R –One of most widely used risk/needs assessments –Includes static and dynamic factors WSIPP removed items that did not have predictive usefulness WSIPP added items that improved predictive accuracy –Detailed juvenile and adult criminal history items The resulting tool uses static factors only WSIPP tool predicts reconviction (arrest data not available)

10 CSRA Uses Multiple Data Sources CDCR OBIS –Demographics –Return to custody outcomes DOJ Automated Criminal History (“Rap Sheets”) –Arrests –Convictions –Parole/probation violations Juvenile criminal history data not available (reliably) in California

11 Test Development Followed Standard Procedure Large sample of 103,000 individuals released from CDCR institution in FY ‘02/’03 Sample divided randomly into construction and validation groups Developed items and weights on the construction group Validated instrument on the validation group

12 UCI Refined the Model to Fit the California Population Test the predictive power of the Washington tool’s items and scales using available CA data –No juvenile criminal history record data Examine CDCR data to see if they had items that added predictive power to the instrument Experiment with different cut points within items and counting rules within the prediction model Weight items based on the strength of their relationships to recidivism Develop predictive models for arrests, reconvictions, and returns to custody

13 Resulting CSRA Uses 22 Items to Predict Recidivism Demographics –Age at release, gender Number of felony sentences Felony sentences for murder/ manslaughter, sex, violent, weapons, property, drug and escape offenses Misdemeanor sentences for assault, sex, weapons, property, drug, alcohol and escape offenses Revocations of probation or parole supervision The model: Risk (felony arrest)= age + gender +…# of violent felony convictions +…# of misdemeanor property convictions +…# of probation/parole violations

14 CSRA Scores Offenders on Three “Nested” Sub-Scales 1.Violent Sub-Scale 2.Property & Violent Sub-Scale 3.Any Felony Sub-Scale This allows CDCR to differentiate risk by type of recidivism

15 CSRA Risk Group Is Determined Hierarchically Violent Score 103 or higher? Yes High Violent No Property/Viol. Score 119 or higher? Yes High Property No Felony Score 127 or higher? Yes High Drug No Property/Viol. Score or Felony Score 96 or higher? Yes Moderate No Low

16 CSRA Divides the Population into Distinct Risk Groups

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18 Appendices

19 CSRA Reconviction Prediction

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21 CSRA Performs within Usual Range for Risk Assessments InstrumentAUCSampleRecidivism Measure Source CSRA ,000 releasees Felony arrestCurrent COMPAS California parolees Return to prisonFarabee and Zhang (2007) Criminal History Computation ,519 Federal offenders Re-conviction, re-arrest w/out dispo. available, supervision revocation US Sentencing Commission (2004) LSI-R0.6722,533 Wash. offenders Any convictionWSIPP (2003) Washington Static Risk Assessment ,648 Wash. offenders Felony convictionWSIPP (2007

22 Sample Item Weights— Felony Scale

23 Sample Item Weights— Violent Scale


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