Addressing racial bias in parole decisions: A pre-registered study of the Five-Level Risk and Needs System of risk communication
Bibliographic record
Abstract
Indigenous peoples are disproportionately influenced by biases in risk assessment and risk communication. The Five-Level Risk and Needs System (5-levels) is a risk communication strategy that adds standardized definitions to risk categories and incorporates evidence-based recommendations for rehabilitation. The current pre-registered study examined the ability of the 5-levels to mitigate biases in a mock parole case using a 2 (risk communication format: status quo vs. 5-levels) × 2 (race: Indigenous vs. White) between-subjects design. Four hundred and twenty-three community members residing in Canada were asked to make decisions regarding parole and other risk, treatment amenability, and report utility outcomes. Risk communication format had no effect on parole. However, participants in the 5-levels condition did provide more accurate risk information than those in the status quo condition. The 5-levels system was also rated lower in terms of understandability and resulted in lower levels of confidence in decisions. Contrary to expectations, participants assigned to the case involving an Indigenous client were more likely to grant parole and provided more favourable ratings of the client compared to participants assigned the case of a White client. Findings from this study may inform risk communication strategies resulting in more consistent and reliable decision-making.
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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 | 0.023 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".