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

Anika Sharma, Snehalkumar 'Neil' S. Gaikwad, Snigdha Chaturvedi, Shashank Srivastava

This paper investigates the Women-Are-Wonderful (WaW) effect in language-model blame attribution: a favorable bias in which women receive more positive evaluations than men. Using 11,270 interpersonal conflicts from r/AmItheAsshole, we construct matched counterfactuals that vary the gender of the person being judged (the original poster) and the person affected (the counter-party). Across four language models, gender changes a minority of verdicts, but those changes consistently favor women. The effect is strongly role-dependent. Making the original poster a woman reduces the odds of blame by 9--52%, while making the counter-party a woman multiplies them by 2.7--9.2X. Counter-party WaW also varies across conflict types, strengthening in communication and relationship conflicts and weakening in family conflicts. Exploratory probes suggest that counter-party gender can shift judgments of sympathy, reasonableness, harm and protection. WaW is therefore not just leniency toward women being judged: models also judge conduct more harshly when it affects a woman.