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
The paper shows that large language models can infer ethnicity from names with higher accuracy than Bayesian Improved Surname Geocoding (BISG), achieving up to 84.7% accuracy on balanced samples from Florida and North Carolina voter files. Six models, including Gemini 3 Flash, GPT‑4o, and open‑source alternatives, were tested, with extended reasoning and metadata such as party registration further improving performance. The method also reduces income bias present in BISG and is validated across diverse populations in India, Uganda, Nepal, Armenia, Chile, and Costa Rica, with fine‑tuned small transformer models surpassing BISG while allowing local deployment at no cost.
By Noah Dasanaike
arXiv:2504. 21259v2 Announce Type: replace-cross Abstract: Accurate imputation of race and ethnicity (R&E) is essential for fair lending compliance under ECOA, HMDA, and the Community Reinvestment Act, where up to 15% of mortgage applications carry missing race data and regulated institutions bear responsibility for identifying disparities on those records.
By S. Chalavadi, A. Pastor, T. Leitch
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
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...
SAGE (Subpopulation-Aware Generative Enhancement) is a two-stage generative augmentation framework designed to mitigate spurious correlations in machine learning when group labels are unavailable. It uses cluster-derived sub-labels and class labels to fine‑tune a conditional generative model and text encoder, producing synthetic data that fills underrepresented regions and creates a balanced validation set for last‑layer reweighting. Experiments show SAGE improves worst‑group accuracy to 89.5%, 85.7%, and 79.1% on Waterbirds, CelebA, and MetaShift, outperforming existing group‑label‑free baselines by up to 7.7 percentage points.
By Yiming Luo, Rongqiang Zhao, Jie Liu
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
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.
By Ilona van der Linden, Sahana Kumar, Arnav Dixit, Aadi Sudan, Smruthi Danda, David C. Anastasiu, Kai Lukoff
arXiv:2608.30688v1 Announce Type: new
Abstract: While face recognition systems are widely deployed, ensuring their demographic reliability and robustness under uncontrolled visual conditions remains...
By Alexandre Diano, Bernardo Biesseck, Gabriel Polo, Vinicius Gregorio, Laura Lopes, Diego Addan, David Menotti
arXiv:2610.17113v1 Announce Type: cross
Abstract: Information extraction models often perform well on the datasets they are trained on, but transferring to new domains and annotation guidelines remai...
By Yang Wang, Hanlin Xiao, Yanan Ma, Chenghao Xiao, Chenghua Lin
While face recognition systems are widely deployed, ensuring their demographic reliability and robustness under uncontrolled visual conditions remains a critical challenge. To bridge this gap, we pres...
The study audits demographic leakage in German-language resumes generated by large language models. Using ChatGPT, Gemini, and Qwen 3 variants, the authors generate resumes from anonymized profiles, varying only gender- and ethnicity-associated names while keeping qualifications constant. Even after anonymization and gender-neutralization, classifiers can reliably distinguish male- from female-generated resumes, driven by subtle differences in gender-neutral terminology rather than overtly gendered wording; ethnicity-related leakage remains weak.
By Charlotte Leininger, Helena Veit, Matthias A{\ss}enmacher, Andreas Bender