Cited by 5Digitalization, Law, and RegulationEthics and Social Impacts of AIArtificial Intelligence in Law

Generative AI and criminal law

Open access
Panattoni, Beatrice·2025·Cambridge Forum on AI: Law and Governance

Abstract

Several criminal offenses can originate from or culminate with the creation of content. Sexual abuse can be committed by producing intimate materials without the subject’s consent, while incitement to violence or self-harm can begin with a conversation. When the task of generating content is entrusted to artificial intelligence (AI), it becomes necessary to explore the risks of this technology. AI changes criminal affordances because it creates new kinds of harmful content, it amplifies the range of recipients, and it can exploit cognitive vulnerabilities to manipulate user behavior. Given this evolving landscape, the question is whether policies aimed at fighting Generative AI-related harms should include criminal law. The bulk of criminal law scholarship to date would not criminalize AI harms on the theory that AI lacks moral agency. Even so, the field of AI might need criminal law, precisely because it entails a moral responsibility. When a serious harm occurs, responsibility needs to be distributed considering the guilt of the agents involved, and, if it is lacking, it needs to fall back because of their innocence. Thus, legal systems need to start exploring whether and how guilt can be preserved when the actus reus is completely or partially delegated to Generative AI.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1017/cfl.2024.9

Cites

45 of 45 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • The cases described by participants sit primarily within Panattoni's malicious dimension and suggest that generative AI extends existing offence categories rather than constituting wholly new ones.

    “The cases described by participants sit primarily within Panattoni's malicious dimension and, on this reading, suggest that generative AI extends existing offence categories rather than constituting wholly new ones.”

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 9

  1. Askell, A., Brundage, M, and Hadfield, G K. (2019). The Role of Cooperation in Responsible AI Development. https://api. semanticscholar.org/CorpusID:195874137
  2. Beck, S., & Gerndt, S. (2023). German report on traditional criminal law categories and AI. Revue Internationale de Droit Pénal, 94(1), 195-223.
  3. Flor, R. (2024). Cybersecurity for artificial intelligence, medical device security and criminal law: First thoughts in the prism of European Law. In P. Magagna, & R. Magagna (Eds.), Advanced diagnostic tools in aortic pathology (pp. 142-149). Edizioni Minerva Medica.
  4. Gehman, S. (2020). Real toxicity prompts: Evaluating neural toxic degeneration in language models. In T. Cohn, Y. He, Y. Liu (Eds.). Findings of the association for computational linguistics (pp. 3356-3369). The Association for Computational Linguistics.
  5. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative Adversarial Nets. In Z. Ghahramani (Eds.), Advances in Neural Information Processing Systems (pp. 1-9).
  6. Miró-Llinares, F., Duvac, C., Toader, T., et al. (Eds.) (2024). Criminalisation of AI-related offence [Monograph]. Revue Internationale de Droit Pénal, 1, 1-445.
  7. Mongillo, V. (2023). Corporate criminal liability for AI-related crimes: Possible legal techniques and obstacles. International Review of Penal Law, 1, 77-90.
  8. Stewart, J. G. (2015). Complicity. In M. D. Dubber & T. H ö rnle (Eds.), The Oxford handbook of criminal law (pp. 534-559). Oxford University Press.
  9. Surden, H. (2024). ChatGPT, artificial intelligence (AI) large language models, and law. Fordham Law Review, 92, 1940-1970.

Authors
Panattoni, B.
Affiliations
University of Verona (IT)
Corresponding author
Panattoni, Beatrice
Venue
Cambridge Forum on AI: Law and Governance
Published
2025
Volume
1
ISSN
3033-3733 (ISSN-L)
OpenAlex
W4406078431
Cited by
5
References
45
Citations by year
2026: 4 · 2025: 1
FWCI
31.30
Type
journal article
Language
en
Open access
Locations
  • Cambridge Forum on AI Law and Governance (publishedVersion, cc-by)
  • Open Repository and Bibliography (University of Luxembourg) (submittedVersion, cc-by)
Topics
  • Social Sciences › Law › Digitalization, Law, and Regulation
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Social Sciences › Political Science and International Relations › Artificial Intelligence in Law
Keywords
generative AI, criminal law, criminal liability, moral agency, moral responsibility, sexual abuse, incitement to violence, self-harm, harmful content, cognitive manipulation, guilt, actus reus

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review