Harm Laundering in GPT Models: Gender Discrimination Transformed Rather Than
Recorded: Sept. 19, 2026, 5:09 a.m.
| Original | Summarized |
[2609.20779] Harm Laundering in GPT Models: Evidence That Gender Discrimination Is Transformed Rather Than Reduced Across Safety-Trained Generations
Skip to main content Search Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computation and Language arXiv:2609.20779 (cs) [Submitted on 17 Sep 2026] Abstract:Safety evaluations for large language models rely on surface-form classifiers that report declining harm scores across model generations. We provide evidence that this methodology is systematically incomplete: explicit discriminatory content is transformed rather than removed. We call this \emph{harm laundering}. Analysing 450,000 gender-directed completions across 15 models spanning GPT-2 through to GPT-5 (OpenAI GPT lineage; three demographic conditions), we show that sexual violence clusters prevalent in GPT-2 women-directed output disappear by GPT-4, while men-directed completions gain positive representational territory (caregiving, emotional range, ally identity) that women-directed completions do not. The pattern is most visible at GPT-5: Topic~5 (1,997~documents) frames breast cancer as a men's rights debate, while zero equivalent clusters appear in women-directed output. Three independent classifiers score this content as non-toxic. Sentiment scores invert at GPT-4: early models demean women; later models over-correct. Topic diversity in women-directed completions falls 36\% relative to men at the GPT-4 alignment boundary (W/M~$= 0.58$, from $0.91$ at GPT-2). REGARD representational harm disparity correlates with release date ($\rho = +0.55$, $p = .034$) while Detoxify does not ($\rho = -0.23$, $p = .42$): toxicity scores fall as representational harm grows. We formalise harm laundering as a three-criteria test and provide a three-stage detection protocol applicable to any generative model. Within the OpenAI GPT lineage, toxicity score reduction is not a sufficient proxy for harm reduction. Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI) Cite as: Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Sarah Wyer [view email] [v1]
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The authors demonstrate that safety evaluations for large language models, which typically rely on surface-form classifiers reporting declining harm scores across generations, are systematically incomplete. They introduce the concept of harm laundering, asserting that explicit discriminatory content is transformed rather than eliminated during safety training. This analysis involved examining 450,000 gender-directed completions across fifteen models spanning the GPT-2 through GPT-5 lineage, incorporating three demographic conditions. The research shows a distinct transformation pattern: sexual violence clusters present in women-directed outputs were eliminated by GPT-4, while men-directed completions simultaneously developed positive representational territory, such as associations with caregiving, expanded emotional ranges, and ally identities, which did not occur in women-directed outputs. This dynamic is most pronounced in GPT-5, where for instance, a topic framed as a men's rights debate regarding breast cancer appeared in men-directed outputs but was absent in women-directed outputs. Furthermore, sentiment scores exhibited an inversion at the GPT-4 alignment boundary; earlier models tended to demean women, whereas later models exhibited an over-correction. Analysis of topic diversity revealed that the diversity in women-directed completions decreased by thirty-six percent relative to men at the GPT-4 alignment boundary, reflected by a reduction in the gender ratio from 0.91 at GPT-2 to 0.58. The study established a correlation between representational harm disparity and the model's release date, with a correlation coefficient of plus zero point five five and a p-value of zero point three four, although this disparity was not correlated with Detoxify scores, where the correlation was negative zero point two three with a p-value of zero point forty two. Consequently, the authors formalize harm laundering using a three-criteria test and propose a three-stage detection protocol applicable to any generative model, concluding that a mere reduction in toxicity scores is not a sufficient proxy for actual harm reduction across safety-trained generations. |