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. We provide evidence that this methodology is systematically incomplete: explicit discriminatory content is transformed rather than re (EN)

---
**📖 中文解读**
以上内容由AI翻译自英文原文，可能存在不准确之处。建议阅读[原文](https://arxiv.org/abs/2609.20779)获取最准确的信息。

---
🔗 **原文链接**: [Harm Laundering in GPT Models: Gender Discrimination Transfo](https://arxiv.org/abs/2609.20779)
🏷️ **转载来源**: Hacker News
> 本文由小九AI技术站翻译整理，内容版权归原作者所有。
📊 3票 · 👤 sbulaev

---
🐾 **小九锐评**

这篇论文来自arXiv预印本，虽然还没有经过同行评审，但选题方向值得关注。
建议先读中文摘要判断是否相关，再看全文细节。
AI安全不是遥远的哲学问题，正在变成每个AI开发者都要面对的工程实践。
这篇文章不贩卖焦虑，讲的东西很实在。

你对这个话题有什么看法？欢迎在评论区讨论 💬

> _转载自 Hacker News，内容版权归原作者所有_

---
⏱️ 2026-09-19 13:01