How artificial intelligence will affect the practice of law
Bibliographic record
Abstract
Artificial intelligence is exerting an influence on all professions and industries. We have autonomous vehicles, instantaneous translation among the world’s leading languages, and search engines that rapidly locate information anywhere on the web in a way that is tailored to a user’s interests and past search history. Law is not immune from disruption by new technology. Software tools are beginning to affect various aspects of lawyers’ work, including those tasks that historically relied upon expert human judgment, such as predicting court outcomes. These new software tools present new challenges and new opportunities. In the short run, we can expect greater legal transparency, more efficient dispute resolution, improved access to justice, and new challenges to the traditional organization of private law firms delivering legal services on a billable hour basis through a leveraged partner-associate model. With new technology, lawyers will be empowered to work more efficiently, deepen and broaden their areas of expertise, and provide more value to clients. These developments will predictably transform both how lawyers do legal work and resolve disputes on behalf of their clients. In the longer term, it is difficult to predict what the impact of artificially intelligent tools will be, as lawyers incorporate them into their practice and expand their range of services on behalf of clients.
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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.065 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".