Artificial Intelligence & Criminal Justice: Cases and Commentary
Bibliographic record
Abstract
When I was given the chance to develop a seminar this year at UBC’s Peter A. Allard School of Law, I jumped at the opportunity to develop something new and engaging. After brainstorming ideas with students, it quickly became evident that there was substantial interest and enthusiasm for a seminar on the growing integration of artificial intelligence and the criminal justice system. Embarking on this journey has been a steep learning curve for me as my students and I worked together to shape the course along with input from generative AI tools like ChatGPT, Gemini and Perplexity, along with open-source materials from the Canadian Legal Information Institute and the Creative Commons search portal. Delving into the case law in Canada and the U.S., reading the critical commentary, listening to podcasts and webinars, and playing around with the latest AI tools has been a lot of fun, but also made me realize how crucial it is at this point in time to have a focussed critical exploration of the benefits and risks of AI in the criminal justice context. I hope that this open access casebook will be a valuable resource for students, instructors, legal practitioners and the public, offering insights into how AI is already influencing various aspects of the criminal justice lifecycle – including criminality and victimization, access to justice, policing, lawyering, adjudication, and corrections. If you’re interested in a quick overview of topics covered in this casebook, you can download the companion: Artificial Intelligence & Criminal Justice: A Primer (2024).
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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 | 0.006 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.014 | 0.017 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.028 | 0.022 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".