Bibliographic record
Abstract
Litigation is a creature of disagreement. Our essay explores the potential of artificial intelligence (AI) to help reduce legal disagreements. In any litigation, parties disagree over the facts, the law, or how the law applies to the facts. The source of the parties' disagreements matters. It may determine the extent to which AI can help resolve their disputes. AI is helpful in clarifying the parties' misunderstanding over how well-defined questions of law apply to their facts. But AI may be less helpful when parties disagree on questions of fact where the prevailing facts dictate the legal outcome. The private nature of information underlying these factual disagreements typically fall outside the strengths of AI's computational leverage over publicly available data. A further complication: parties may disagree about which rule should govern the dispute, which can arise irrespective of whether they agree or disagree over questions of facts. Accordingly, while AI can provide clarity over legal precedent, it often may be insufficient to provide clarity over legal disputes. This article is part of the theme issue 'A complexity science approach to law and governance'.
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 | 0.045 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.016 | 0.079 |
| Scholarly communication | 0.021 | 0.028 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.014 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 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".