Justice as a Rounding Error?: Evidence of Subconscious Bias in Second-Degree Murder Sentences in Canada
Bibliographic record
Abstract
There are few areas of law that grant judges as much discretion as the sentencing of criminal offenders. This discretion necessarily leads to concerns about the influence of biases, including those that result from subconscious processes associated with human cognition; that is to say, heuristics. In this article, the authors explore one heuristic—number preference—through an examination of all reported second degree murder parole ineligibility decisions between 1990 and 2012. Number preference leads individuals to predictably round off measurements to certain favoured numbers. The authors identify a tendency for parole ineligibility decisions to cluster around even numbers and multiples of five, without any obvious, legally-justifiable reason for such rounding. The authors propose that the phenomenon should cause concern not least because it suggests that other, less easily measurable but no less powerful, heuristics may also be at work in judicial decisions.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".