arXiv:2610.19028v1 Announce Type: new
Abstract: Science fiction is widely credited with unsettling gender. This article asks whether that holds at the linguistic level, in how readily gendered refere...
By Yuxuan Liu, Haim Dubossarsky, Ruth Ahnert
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...
By Yingjia Wan, Lin Lin, Elisa Kreiss
The study introduces GAPA, a dataset of 316 physical attributes with 14,706 gender-association ratings from 304 US annotators, showing that such descriptions carry structured gender associations. It evaluates 16 LLMs, finding they partially mirror human ratings but exhibit biases such as compressed distributions, weaker alignment for men, and asymmetric abstention toward non‑binary identities. A proxy model trained on these data is released and applied to analyze character descriptions in LitBank, illustrating the persistence of gendered interpretations in ostensibly neutral language.
By Yingjia Wan, Lin Lin, Elisa Kreiss
The study evaluates name-based bias in six local large language models using a fill-in-the-blank task with first names from U.S. Social Security and a race classification dataset. Across 300,000 responses, the models consistently favored names that were less stereotypically male, older, popular, and white, with gender bias being the strongest effect. The bias varied with sentence context but remained significant, underscoring the persistence of name bias in local LLMs.
By Kalyan Bhetwal, Ceren Mert, Ian Whitehouse
The paper demonstrates that pre‑trained language models can perform record linkage and demographic inference from names without task‑specific training. EnsembleLink, an algorithm that combines pre‑trained embeddings with string‑based scorers, achieves performance comparable to or better than prior methods across diverse scripts and formats. Large language models reach up to 83.8% accuracy on U.S. voter files for ethnicity inference, surpassing name‑supplemented BISG and enabling inference for non‑U.S. ethnic categories while reducing bias in geographic racial gap estimates.
By Noah Dasanaike
Names carry demographic indicators such as age, gender, and race, which makes them a subtle but strong target of bias in large language models (LLMs). In this work, we present an evaluation of name-ba...