The Use of AI in Canadian Courts
Bibliographic record
Abstract
Like many other fields, there has been growing discussion about the potential benefits of AI for the law. In light of the Federal Court’s interim principles and guidelines on the use of AI, this paper considers whether AI applications can assist the judiciary with its decision-making function. In doing so, it starts by considering the role that judges play in our legal system, finding that they are often called upon to consider and weigh information with human, emotional qualities and to assess the broader policy implications of their legal rulings. This paper concludes that the optimism of proponents of AI in the courtroom may be misplaced. The mathematical, mechanistic decision-making of AI applications does not replicate the kind of decisions judges are called upon to make. Moreover, it may be dangerous. Humans tend to defer to recommendations produced by algorithms, and AI applications are so complex that perhaps no one person—and certainly no one in the legal field—has the expertise necessary to understand how and why an AI application has reached a particular recommendation. Instead, the solution to lessening the burden on our legal system may be the more obvious, but less trendy question, of funding. More judges and more court staff may go much farther.
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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 | 0.012 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.023 | 0.012 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".