Introduction: Artificial intelligence, technology, and the 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
On 25 March 2017, the Centre for Innovation Law and Policy at the University of Toronto hosted a Conference on Artificial Intelligence, Technology, and the Law. The conference was supported by generous funding from the University of Toronto Press and from the Social Science and Humanities Research Council of Canada. The contributions to this special issue of the UTLJ are based on articles originally presented at the conference. Some of the speakers discussed the kinds of tasks that machine learning (ML) and natural language processing (NLP) can perform, when used to conduct legal research, to identify biases and discrepancies at the doctrinal level and in the performance of lawyers and judges, and to facilitate access to justice for those who cannot readily afford legal services. Other speakers considered the challenges that algorithms based on ML and NLP pose to democratic conceptions of legal authority. Taken together, the articles offered a range of views on the prospects and perils of AI for the practice of law and for the legal system as a whole. This introduction briefly describes the contributions, moving roughly from the more theoretical to the more concrete aspects of these issues.
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 | 0.003 | 0.012 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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