Artificial Intelligence-Based Deepfake Crimes: A Conception of Culpability Principle as a Criminal Liability Reform
Bibliographic record
Abstract
The phenomenon of deepfake crimes based on artificial intelligence (AI) demands a reform of criminal liability concepts through the expansion of the culpability principle, allowing the placement of AI as a subject of law. However, the idea of recognizing AI as an independent legal entity (electronic personhood) is considered irrelevant, since AI lacks human-like will and moral autonomy. Therefore, this study proposes a model of criminal liability that extends the culpability principle to providers and users of deepfake technology. Using a normative legal research method based on primary and secondary legal materials, this study comprehensively examines the application of the culpability principle through a comparative approach among various jurisdictions. The findings indicate that the most proportional form of liability is the vicarious liability model, which was initially applied to corporations but can be adapted to the AI context. In this model, software providers may be held liable for acts committed by AI in deepfake crimes, particularly as part of their responsibility toward technology governance regulations. The study recommends establishing national regulations emphasizing governance systems based on risk assessment, risk management, and impact assessment, as practiced in the European Union, Canada, and the United States. In conclusion, reforming criminal liability in the AI era is a strategic step to address the growing prevalence of deepfake crimes and to ensure that the legal system remains adaptive to technological developments.
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 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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.034 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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".