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: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: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...
arXiv:2610.23587v1 Announce Type: new Abstract: Post-training is widely assumed to reduce the social biases language models inherit from pretraining, yet direct evidence for this assumption remains m...
Large language models (LLMs) are increasingly used as judges for subjective tasks, where annotators disagree and the relevant question is not only how accurate a judge is, but whose judgments it repro...
The paper reports the first systematic audit of open‑weight large language models (LLMs) in hiring contexts, examining how job‑posting language influences recruiter and job‑seeker simulations across six models. It finds that agentic language lowers recruiter scores for female candidates while communal language mitigates this effect, and that coded‑exclusion language sharply reduces recruiter scores for non‑White candidates and discourages non‑White personas from applying. The study also identifies the explicit demographic label as the main causal factor and proposes a concrete pre‑deployment audit protocol aligned with EU and U.S. regulatory requirements.
The paper examines how large language models (LLMs) respond to different demographic cues—such as names—when users seek advice, focusing on race and gender in a U.S. context. It finds that using different cues for the same group leads to only partially overlapping changes in model responses, producing inconsistent conclusions about personalization and unstable bias metrics. The authors argue that LLMs react to linguistic signals tied to cues rather than to stable demographic categories, and they call for evaluations that use multiple cues and consider underlying mechanisms.
The paper presents a multilingual audit of stereotype bias in large language models (LLMs) using 12,000 locally written Reddit posts in English, Spanish, French, and German. It evaluates gender, racial, and religious bias across six models (three open-weight and three proprietary APIs) with a two-blank design to reduce grammatical cues, finding that religion shows the highest imbalance, Spanish leads in gender bias, and German in race and religion. The study highlights that aggregate scores can mask category- and context-specific biases, underscoring the need for detailed inspection before deploying LLMs in consequential settings.
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...
Large language models (LLMs) are increasingly deployed in hiring workflows, yet most research on gender bias in LLM hiring decisions has focused on English-language, Western-format resumes. This study examines whether pro-female gender bias extends to a Japanese corporate context and evaluates two practical mitigation strategies.
arXiv:2502.10577v2 Announce Type: replace-cross Abstract: Instruct-based large language models (LLMs) have been shown to propagate and even amplify gender bias when prompted with contextually constra...
arXiv:2609.16366v1 Announce Type: cross Abstract: When foundation models describe people, recent work in AI fairness, accessibility, and ethics recommends avoiding inferred identity labels (e.g., "sh...