Do Structured Risk Assessments Reduce Bias Against Indigenous Youth? An Experimental Analysis - Study 1
Bibliographic record
Abstract
Racial and ethnic disparities within Western criminal justice systems are unfortunately commonplace. In fact, the overrepresentation and overincarceration of Indigenous youth and adults has been recognized as a crisis by the Supreme Court of Canada in R. v. Gladue (1999). Prior research has found that laypersons as well as youth justice professionals are susceptible to negative biases against racial/ethnic minority youth, however, limited research has focused on Indigenous youth. While some researchers have suggested that structured judgment approaches in risk assessment may be useful in reducing biases, others suggest that the use of such approaches may exacerbate disparities. Given gaps within the literature, the proposed research will use an experimental vignette design to examine racial biases in unstructured risk judgments (i.e., judgments based solely on subjective intuition) versus structured risk judgments (i.e., judgments based on a risk assessment tool) for Indigenous youth compared to White youth. Participants recruited through a Canadian university will be randomly assigned with identical vignettes that vary by race (Indigenous or White) and with either unstructured or structured risk judgment conditions. All components of the study are completed online through Qualtrics. The aim of the proposed research is to examine if Indigenous youth will be rated as higher risk than White youth, and whether these differences will be reduced through the use of a risk assessment tool. Results of the proposed research will shed light on the potential for risk assessment tools to reduce assessors’ racial bias in risk assessment for Indigenous youth.
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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 | 0.035 | 0.102 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".