Aligning Algorithmic Risk Assessments with Criminal Justice Values
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it