Law as computation in the era of artificial legal intelligence: Speaking law to the power of statistics
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
The idea of artificial legal intelligence stems from a previous wave of artificial intelligence, then called jurimetrics. It was based on an algorithmic understanding of law, celebrating logic as the sole ingredient for proper legal argumentation. However, as Oliver Wendell Holmes has noted, the life of the law is experience rather than merely logic. Machine learning, which determines the current wave of artificial intelligence, is built on data-driven machine experience. The resulting artificial legal intelligence may be far more successful in terms of predicting the content of positive law. In this article, I discuss the assumptions of law and the Rule of Law and confront them with those of computational systems. As a twin article to my Chorley lecture on law as information, this should inform the extent to which artificial legal intelligence provides for responsible innovation in legal decision making.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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