Harm Laundering in GPT Models: Gender Discrimination Transformed Rather Than
We provide evidence that this methodology is systematically incomplete: explicit discriminatory content is transformed rather than removed. 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.
- ▪We provide evidence that this methodology is systematically incomplete: explicit discriminatory content is transformed rather than removed.
- ▪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.
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| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2609.20779 |
| Publication time | Sat, 19 Sep 2026 04:07:08 +0000 |
| Retrieval time | 2026-09-19T05:03:46.347Z |
| Last seen | 2026-09-19T05:03:46.347Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | WXqAFuKoPpe8 · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Computation and Language arXiv:2609.20779 (cs) [Submitted on 17 Sep 2026] Title:Harm Laundering in GPT Models: Evidence That Gender Discrimination Is Transformed Rather Than Reduced Across Safety-Trained Generations Authors:Sarah Wyer, Sue Black, Noura Al Moubayed View a PDF of the paper titled Harm Laundering in GPT Models: Evidence That Gender Discrimination Is Transformed Rather Than Reduced Across Safety-Trained Generations, by Sarah Wyer and 2 other authors View PDF HTML (experimental) Abstract:Safety evaluations for large language models rely on surface-form classifiers that report declining harm scores across model generations.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv.org.
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