Bibliographic record
Abstract
In 2023, it was uncovered that two New York attorneys had cited fictitious judgments in their filings, exposing their use of generative artificial intelligence (“GenAI”) in document preparation. Similar incidents soon emerged in Texas, Colorado, and California in the United States, and Canada. Additionally, two judges in England and Colombia admitted to using GenAI in their judicial processes, leading to concerns that the technology’s rapid adoption outpaces understanding of its risks. Corroborating this view, data from Wolters Kluwer’s 2023 “Future Ready Lawyer” survey revealed that 73% of 700 surveyed lawyers intend to integrate GenAI into their legal practices within the next 12 months. Another survey, conducted by LexisNexis in 2024 with 266 senior managing lawyers, indicated that law firms and corporate legal departments plan to increase their investment in GenAI by 90% over the next five years. The release of similar surveys has polarized the legal community over GenAI. While some legal professionals advocate for GenAI, citing its efficiency and effectiveness, others voice concerns over its reliability, consistency, and potential biases. At this stage, GenAI’s integration into legal practice appears inevitable, with ongoing debates likely confined to academic circles. The pressing issue now is not whether GenAI should be used but how it is employed by judges and lawyers and how this use will be addressed and governed. This article explores these questions, examining the current and potential uses of GenAI in legal practice, the regulatory steps taken in the U.S., Canada, and the European Union, and the possible steps Türkiye can take in response.
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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 | 0.026 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.052 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".