arXiv Machine Learning September 22, 2026 By Charlotte Leininger, Helena Veit, Matthias A{\ss}enmacher, Andreas Bender

Fairness Beyond Anonymization? Demographic Leakage in German LLM-Generated Resumes

Read the original on arXiv Machine Learning →

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.

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