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.\nEmbarking 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.\nDelving 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.\nI 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).
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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; both teacher heads agree on what is shown here.
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".