Artificial Intelligence and the Legal Profession
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
How are new technologies changing the practice of law? With examples and explanations drawn from the UK, US, Canada, Australia and other common law countries, as well as from China and Europe, this book considers the opportunities and implications for lawyers as artificial intelligence systems become commonplace in legal service delivery. It examines what lawyers do in the practice of law and where AI will impact this work. It also explains the important continuing role of the lawyer in an AI world. This book is divided into three parts: Part A provides an accessible explanation of AI, including diagrams, and contrasts this with the role and work of lawyers. Part B focuses on six different aspects of legal work (litigation, transactional, dispute resolution, regulation and compliance, criminal law, and legal advice and strategy) where AI is making a considerable impact and looks at how this is occurring. Part C discusses
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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