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 behavior
Maryland 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
Violent Sub-Scale
Property & Violent Sub-Scale
Any Felony Sub-Scale
This allows CDCR to differentiate risk by type of recidivism

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

16. CSRA Divides the Population into Distinct Risk Groups

17. CSRA Divides the Population into Distinct Risk Groups

18. Appendices

19. CSRA Reconviction Prediction

20. CSRA Reconviction Prediction

21. CSRA Performs within Usual Range for Risk Assessments
Instrument
AUC
Sample
Recidivism Measure
Source
CSRA
0.70
103,000 releasees
Felony arrest
Current
COMPAS
0.67
515 California parolees
Return to prison
Farabee and Zhang (2007)
Criminal History Computation
0.68
28,519 Federal offenders
Re-conviction, re-arrest w/out dispo. available, supervision revocation
US Sentencing Commission (2004)
LSI-R
22,533 Wash. offenders
Any conviction
WSIPP (2003)
Washington Static Risk Assessment
0.74
51,648 Wash. offenders
Felony conviction
WSIPP (2007

22. Sample Item Weights—Felony Scale

23. Sample Item Weights—Violent Scale