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.
arXiv:2609.38036v2 Announce Type: replace-cross Abstract: Understanding gender biases in large language models (LLMs) is increasingly important as these systems become embedded in decision-support to...
arXiv:2609.38036v1 Announce Type: cross Abstract: Understanding gender biases in large language models (LLMs) is increasingly important as these systems become embedded in decision-support tools with...
arXiv:2610.15961v1 Announce Type: cross Abstract: Emergent misalignment (EM) occurs when fine-tuning a language model to a narrow task makes it misaligned on unrelated ones. Because organizations now...
arXiv:2608. 03627v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used for automated fact-checking, yet their susceptibility to gender bias in this context remains underexplored.
arXiv:2611.02491v1 Announce Type: new Abstract: Large language models are increasingly used as measurement instruments for socially consequential constructs such as hate speech, and are typically val...
The study investigates how gender assignments of candidates and evaluators influence decisions made by large language models (LLMs) in a city council voting scenario. Eight LLM-as-a-Judge and decision language models evaluated 240 synthetic professional narratives, producing 30,720 judgments. Results show that explicit candidate gender labels slightly increased the probability of a Yes vote, only nonbinary evaluator assignments had a significant effect, and decision reversals varied across models, with decision language models exhibiting fewer reversals than LLM-as-a-Judge models.
arXiv:2607. 28934v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly involved in the distribution of scarce resources, raising concerns about biased allocations based on characteristics like race and gender.
The study investigates how speech‑to‑speech (S2S) models handle gender, distinguishing between the acoustic voice and the content’s gender cues. Experiments across five models in English, Spanish, and Mandarin show that while the rendered voice remains unbiased, the models consistently attribute speaker gender based on textual content rather than voice. When content and voice disagree, misgendering rates soar to 90%, whereas agreement yields only 2% misgendering.
arXiv:2512. 15792v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have rapidly become indispensable tools for acquiring information and supporting human decision-making.
arXiv:2610.33021v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly deliver information through personas, but it is unclear whether correcting the facts also makes their frami...
arXiv:2603. 13891v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used for automated text annotation in tasks ranging from academic research to content moderation and hiring.
arXiv:2609.00222v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as judges for subjective tasks, where annotators disagree and the relevant question is not only how...