Are risk assessments racially biased?: Field study of the SAVRY and YLS/CMI in probation.
Bibliographic record
Abstract
Risk assessment instruments are widely used by juvenile probation officers (JPOs) to make case management decisions; however, few studies have investigated whether these instruments maintain their predictive validity when completed by JPOs in the field. Moreover, the validity of these instruments for use with minority groups has been called into question. This field study examined the predictive validity of both the Structured Assessment of Violence Risk in Youth (SAVRY; n = 383) and the Youth Level of Service/Case Management Inventory (YLS/CMI; n = 359) for reoffending when completed by JPOs. The study also compared Black and White youth to examine the presence of test bias. The SAVRY and YLS/CMI significantly predicted reoffending at the test level, with most of the variance in reoffending accounted for by dynamic risk scales not static scales. The instruments did not differentially predict reoffending as a function of race but Black youth scored higher than White youth on the YLS/CMI scale related to official juvenile history. The implications for use of risk assessments in the field are discussed. (PsycINFO Database Record
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".