arXiv Computation and Language November 2, 2026 By Anika Sharma, Snehalkumar 'Neil' S. Gaikwad, Snigdha Chaturvedi, Shashank Srivastava

Language Models Show a "Women-Are-Wonderful" Effect in Blame Attribution That is Not Just Leniency

Read the original on arXiv Computation and Language →

The study examines the Women‑Are‑Wonderful (WaW) effect in language‑model blame attribution, finding that women receive more favorable evaluations than men in interpersonal conflict scenarios. Using 11,270 r/AmItheAsshole posts and four language models, the authors show that changing the gender of the original poster or counter‑party alters blame verdicts, with women’s presence reducing blame odds for the poster and increasing them for the counter‑party. The effect varies by role and conflict type, and is linked to shifts in judgments of sympathy, reasonableness, harm, and protection.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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