Artificial Intelligence, Mindreading, and Reasoning in Law
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
One aspect of legal reasoning is the act of working out another party’s mental states (their beliefs, intentions, etc.) and assessing how their reasoning proceeds given various conditions. This process of “mindreading” would ideally be achievable by means of a strict system of rules allowing us, in a neat and logical way, to determine what is or what will go on in another party’s mind. We argue, however, that commonsense reasoning, and mindreading in particular, are not adequately described in this way: they involve features of uncertainty, defeasibility, vagueness, and even inconsistency that are not characteristic of an adequate formal system. We contend that mindreading is achieved, at least in part, through “mental simulation,” involving, in addition, nested levels of uncertainty and defeasibility. In this way, one party temporarily puts himself or herself certainly in the other party’s shoes, without relying wholly on a neat and explicit system of rules.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it