Harm Laundering in GPT Models: Gender Discrimination Transformed Rather Than
A new academic paper investigates how safety training in GPT models fails to eliminate gender bias, instead transforming it into more subtle forms. The study highlights the ongoing challenge of achieving true fairness in large language models.
Why it matters
As AI models become integrated into critical infrastructure, understanding the persistence of algorithmic bias is essential for ethical development.
Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Sarah Wyer [ view email ] [v1] Thu, 17 Sep 2026 17:49:28 UTC (55 KB) Full-text links: Access Paper: 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) TeX Source view license Current browse context: cs.CL < prev | next > new | recent | 2026-09 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps?
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