Aligning Algorithmic Risk Assessments with Criminal Justice Values
Bibliographic record
Abstract
Abstract Federal and state criminal justice systems use algorithmic risk assessment tools extensively. Much of the existing scholarship on this topic engages in normative and technical analyses of these tools, or seeks to identify best practices for tool design and use. Far less work has been done on how courts and other criminal justice actors perceive and utilize these tools on the ground. This is an important gap. Judges’ and other criminal justice actors’ attitudes toward, and implementation of, algorithmic risk assessment tools profoundly affect how these tools impact defendants, incarceration rates, and the broader criminal justice system. Those who would understand, and potentially seek to improve, the courts’ use of these tools would benefit from more information on how judges actually think about and employ them. This article begins to fill in this picture. The authors surveyed Ohio Courts of Common Pleas judges and staff, and interviewed judges and other key stakeholders, to learn how they view and use algorithmic risk assessment tools. The article describes how Ohio Common Pleas Courts implement algorithmic risk assessment tools and how judges view and utilize the tools and the risk scores they generate. It then compares Ohio practice in this area to the best practices identified in the literature and, on this basis, recommends how the Ohio Courts of Common Pleas—and, by implication, other state and federal court systems—can better align their use of algorithmic risk assessment tools with core criminal justice values.
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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 | 0.098 | 0.219 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.026 | 0.015 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".