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.
By Maikel Leyva-Vazquez, Florentin Smarandache
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.
By Triveni Morla, Rohith Reddy Bellibaltu, Manpreet Singh, Manmeet Singh Kapoor
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.
By Messi H. J. Lee
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.
By Siddharth Vohra, Manikandan Ravikiran
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...
By Matthew Bone, Fabian Stephany, Maria del Rio-Chanona
arXiv:2506. 14092v4 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed in decision-support systems for high-stakes domains such as hiring and university admissions, where choices often involve selecting among competing alternatives.
By Haonan Yin, Shai Vardi, Vidyanand Choudhary
The paper examines whether removing declared language fields from de‑identified résumés eliminates demographic leakage in large language models. By keeping language attributes identical and varying only unstructured prose across five ethnocultural groups and three cue‑salience levels, the authors find that non‑language text still allows target‑group recovery (average 0.757, reaching 1.000 under high salience). They also show that evaluation design—such as allowing or forbidding ties—dramatically affects LLM‑as‑a‑judge outcomes, underscoring the importance of evaluation protocol in bias audits.
By Qiangju Chen, Yang Xiao
arXiv:2608. 14399v1 Announce Type: cross Abstract: Patients increasingly ask large language model (LLM) assistants which doctor to see, making these systems AI infomediaries: algorithms that intermediate one person's choice among other people and thereby decide, silently and at scale, which physicians become visible.
By Syeda Anshrah Gillani, Mirza Samad Ahmed Baig
The study investigates how the identity of a judge influences outcomes in large language model (LLM)-as-judge panels, using a fully crossed pairwise design across four open-weight families (Llama 3.1, Qwen 2.5, Gemma 2, and Yi 1.5) with 9,312 judgments. A common per‑family statistic was found to be strongly confounded with candidate quality, prompting the authors to develop a corrected estimator that isolates judge effects while holding candidate family constant. The corrected analysis reveals a consistent positive same‑family lift (3.4–8.4 percentage points) across all families, with a global effect size of 0.067 (95 % CI [0.053, 0.084]) and a permutation p = 0.0002, and demonstrates that judge‑side likelihood and panel composition significantly influence outcomes.
By David Ababio Awuni, Luke E. K. Achenie, Benjamin Tei Partey, Elvis Gyasi Owusu, Nii-Nai Derrick Sowah
The paper investigates how many hidden model variants can exist while still supporting a published leaderboard margin that shows a provider’s advantage over a fixed comparator. It derives a sensitivity curve for a fixed candidate family under a Gaussian margin model, linking the maximum number of hidden variants to a lower bound on within‑family correlation. Using this framework, an audit of 394 adjacent‑rank claims on the Open LLM Leaderboard found that 391 lack statistical support before any correction, and that certification of the remaining claims depends on assumptions about the hidden family’s correlation.
By Chen Yang, Xianyang Zhang, Jun Chen
arXiv:2608. 05178v1 Announce Type: cross Abstract: Equitable access to scientific knowledge often depends on informal gatekeeping decisions, particularly when resources such as paywalled articles, datasets, or professional materials such as curriculum vitae (CV) must be shared selectively.
By Nouar AlDahoul, Hezerul Abdul Karim, Myles Joshua Toledo Tan
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.
By Messi H. J. Lee