Legal challenges in tackling AI-generated child sexual abuse material within Canada - REPORT
Bibliographic record
Abstract
This report critically reviews the regulatory context of Canada on the topic of accountability around child sexual abuse material (CSAM) created via generative Artificial Intelligence (gen-AI) on a federal and provincial level. The federal Criminal Code lacks specific prohibitions against AI-generated CSAM. Nonetheless, the relevant sections of the Canadian Criminal Code have been interpreted widely by the Supreme Court of Canada to provide coverage for several types of harmful material. However, two exceptions, remain: One for material created only for personal use, and another for works of art that lack intent to exploit children. Canadian federal law criminalises the non-consensual distribution of intimate images. Whether these provisions apply to AI-generated CSAM is uncertain. Privacy laws in Canada offer some avenues for assistance, but they lack a more tailored character to address the specific harms associated with AI-generated CSAM. Copyright law offers a potential, although complex, avenue for addressing AI-generated CSAM.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.019 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 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".