The study reports that white men lacking an undergraduate degree receive the lowest average ratings from large language models across credit, hiring, and rental application scenarios. Using 18 models and 32 demographic profiles, the researchers found that these individuals scored 75.87 in credit, 92.71 in hiring, and 86.62 in rental housing, while black women with degrees scored highest. The findings show that white men without degrees consistently rank lowest or second‑lowest in most model‑scenario combinations, underscoring a distinct disadvantage not captured by broader racial or gender categories.
arXiv:2609.22169v1 Announce Type: new Abstract: Employers are increasingly using large language models (LLMs) to automate their hiring process. This paper investigates the risk of monocultural biases...
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.
arXiv:2510. 21011v3 Announce Type: replace-cross Abstract: As generative AI tools are increasingly used to portray people in professional roles, understanding their racial and gender representational biases is critical.
arXiv:2501. 02211v3 Announce Type: replace-cross Abstract: Large language models (LLMs) reproduce homogeneity bias -- the tendency to portray marginalized groups as more internally similar than dominant groups -- but whether this bias generalizes across models, is stable under different inference settings, or depends on how group identity is signaled remains unstudied.
arXiv:2501. 02211v2 Announce Type: replace-cross Abstract: Large language models (LLMs) reproduce homogeneity bias -- the tendency to portray marginalized groups as more internally similar than dominant groups -- but whether this bias is stable or an artifact of inference settings has only been studied in single proprietary 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.
arXiv:2608. 10089v1 Announce Type: cross Abstract: Bias evaluations often move too quickly from evidence that a model encodes a social association to claims that the same association will alter consequential decisions.
The study examines how the design of audit questions influences perceived bias in large language models (LLMs). Using 40,726 requests across five models and three domains—hiring, lending, and medical triage—the authors find that demographic bias effects are not replicated when the audit is standardized. Instead, the audit’s construction, such as question phrasing and ordering, has a stronger impact on model responses than applicant demographics.
The study examines whether large language models (LLMs) discriminate against candidates based on institutional prestige and geographic location. Three factorial experiments (4,320 API calls across four LLMs and five domains) reveal a significant institution‑tier gradient (+0.297 points on a 10‑point scale), a stronger prestige effect than country‑of‑origin effect, and a dominant journal prestige effect (Nature vs. a peripheral journal). The authors also identify a ‘rescue effect’ where publishing in Nature mitigates low institutional prestige, and quantify bias using the Neutrosophic Bias Index, highlighting evaluation inconsistency for low‑prestige profiles.
arXiv:2606. 16723v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly take actions (screening applicants, recommending credit, triaging patients), yet fairness for LLMs is still measured by grading answers.
The study evaluates gender representation in 8,000 images generated by four generations of the Stable Diffusion text‑to‑image model across 20 occupations and five prompt templates. It finds that 76.4% of the images depict male subjects, with 57.6% of historically female‑coded occupations also showing male subjects, and that newer model generations do not consistently reduce bias. Compared to U.S. Bureau of Labor Statistics data, the models underrepresent women by 20–46 percentage points, especially in near gender‑balanced fields such as scientists and cleaners.
The paper examines how large language models (LLMs) alter the expression of Dark Triad traits—Machiavellianism, narcissism, and psychopathy—when prompted to fake good or fake bad. Across seven state‑of‑the‑art models and two real‑world contexts (employment selection and forensic evaluation), most models lowered trait scores under fake‑good conditions and raised them under fake‑bad conditions, with varying consistency across traits and models. The study also finds that explicit fake‑bad instructions produce stronger distortions than contextual framing alone, underscoring the influence of motivational and situational context on personality‑related outputs.