Bibliographic record
Abstract
Some countries have explored the idea of using artificial intelligence (AI) systems to help triage the backlog of cases and facilitate the resolution of civil disputes. In theory, AI can accomplish this by establishing the facts of cases and predicting the outcomes of disputes. But the use of AI in the courtroom gives rise to new problems. AI technologies help solve prediction problems. These solutions are typically expressed as probabilities. How should judges incorporate these predictions in their decision making? There is no obviously correct approach for converting probabilistic predictions of legal outcomes into binary legal decisions. Any approach that does so has benefits and drawbacks. Importantly, a balance of probabilities approach – where liability is established if the AI predicts a likelihood of liability greater than 50 per cent and not otherwise – is not suitable when converting a predicted outcome into an actual outcome. Adopting this approach would significantly alter the outcomes of legal cases and have a dramatic and disruptive effect upon the law. The most notable disruption would be observed in settlement behaviour and outcomes.
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.
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 | 0.043 | 0.137 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.041 |
| Scholarly communication | 0.011 | 0.031 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.010 | 0.025 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".