Developing an artificial intelligence ethics governance checklist for the legal community
Bibliographic record
Abstract
This study develops a stakeholder-informed artificial intelligence (AI) ethics governance checklist tailored for Canadian law firms to help them harness the productivity and economic advantages of AI while minimizing the risks of unethical outcomes. Recognizing the limitations of existing AI principles (AIPs) in preventing unethical outcomes, this research uses semi-structured interviews, qualitative content analysis, and expert stakeholder engagement to design an eight-page AI ethics governance checklist. In addition to the output of a practical governance checklist, the study reports findings about the development of stakeholder-informed governance checklists. The findings reveal that Canadian lawyers share global concerns surrounding AI risks, including privacy, accountability, safety and security, transparency and explainability, human oversight, professional responsibility, and the promotion of human values. In addition, many law firms interact with AI primarily through third-party vendors, making a principle-based checklist the most practical approach. The research highlights the importance of question format, suggesting that balancing clarity (using Yes/No options) with flexibility (allowing for open-ended comments) is essential, given the complex ethical considerations. The study also finds there is a need to integrate the checklist with existing policies, such as privacy impact assessments and IT risk evaluations, alongside relevant regulatory frameworks. Additionally, tailoring language and definitions to reflect the specific needs of stakeholders (in this case, lawyers) enhances usability and effectiveness. The resulting eight-page, stakeholder-informed AI ethics governance checklist has been adopted by several Canadian law firms and Barristers’ Societies, offering a practical tool to guide the responsible adoption and use of AI in the legal sector.
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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 | 0.127 | 0.266 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.005 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".