The AI-Human Unethicality Gap: Plagiarizing AI-generated Content Is Seen As More Permissible
Bibliographic record
Abstract
The emergence of generative AI has raised unprecedented concerns about plagiarism. We present six preregistered studies demonstrating that plagiarizing material created by AI is seen as less unethical and more permissible than plagiarizing material created by a human—an AI-human unethicality gap. Students report having plagiarized more from AI than human-generated content in the past (Study 1) and indicate greater willingness to do so in their school assignments, even when ease and convenience of accessing such content are held constant (Study 2). Moreover, people judge plagiarizing AI-generated content as less unethical and more permissible than plagiarizing human-generated content and are less likely to view it as plagiarism (Study 3). Rather than being due to differences in legal ownership (Study 4), the AI-human unethicality gap is explained by psychological ownership over the copied material (Studies 4 and 5). AI is perceived as owning the content it creates to a lesser extent than humans: when using content produced by AI (vs. humans), users are afforded greater psychological ownership over the content, reducing the perceived unethicality of passing off the content as their own. Differences in psychological ownership appear to stem from ascriptions of sentience to the content creator: imbuing AI with sentience attenuates differences in perceived ownership and in turn the AI-human unethicality gap (Study 6). These findings contribute to understanding the social effects of AI, attribution of psychological ownership, and navigating plagiarism in the age of AI.
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 | 0.005 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".