Legal challenges in tackling AI-generated CSAM across the UK, USA, Canada, Australia and New Zealand: Who is accountable according to the law?
Bibliographic record
Abstract
This study is among the first to examine the regulatory context of five closely inter-connected countries (UK, USA, Canada, Australia and New Zealand) in terms of accountability around child sexual abuse material (CSAM) created via generative artificial intelligence (gen-AI). The findings of our legislative review have helped us identify the key strengths as well as weaknesses of the legislative contexts studied. These countries were selected due to their democratic political systems, technological advances and progressive legislative systems. We examined 29 pieces of legislation and 25 cases for the UK context; 27 pieces of legislation in Australia and New Zealand, together with 4 emerging cases; as well as 279 statutes, 52 pieces of pending legislation and 65 cases in the US and Canada.
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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 | 0.046 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.014 | 0.024 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| 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".