Racist Algorithms or Systemic Problems? Risk Assessments and Racial Disparities
Bibliographic record
Abstract
As recent and historical events attest, racial and ethnic disparities are widely engrained into the justice system. Recently, scholars and policymakers have raised concerns that risk assessment instruments may exacerbate these disparities. While it is critical that risk instruments be scrutinized for racial bias, some concerns, though well-meaning, have gone beyond the evidence. This article explains what it means for an instrument to be “biased” and why instruments should not all be painted with the same brush (some will be more susceptible to bias than others). If some groups get apprehended more, those groups will score higher on non-biased, well-validated instruments derived to maximize prediction of recidivism because of mathematics. Thus, risk instruments shine a light on long-standing systemic problems of racial disparities. This article concludes with suggestions for research and for minimizing disparities by ensuring that systems use risk assessments to avoid unnecessary incarceration while allowing for structured discretion.
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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 | 0.086 | 0.247 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".