Large language models (LLMs) are increasingly integrated into AI-assisted hiring pipelines, including automated resume generation and screening. Under the EU AI Act, the hiring domain is classified as high-risk, making fairness and transparency critical requirements. Existing work has primarily focused on explicit hiring decisions, while less attention has been paid to whether generated resumes themselves encode recoverable demographic information. In this work, we conduct a two-stage audit of demographic leakage in German-language LLM-generated resumes. First, we use ChatGPT (GPT-4o-mini), Gemini 2.5 Flash-Lite, and multiple scales of the open-weight Qwen 3 model family (4B, 8B, and 14B) to generate resumes from real anonymized job-matching profiles, systematically varying gender- and ethnicity-associated names while holding qualifications constant. Second, we simulate a downstream resume screening scenario, where the generated resumes are first anonymized and gender-neutralized, before demographic leakage classifiers are trained on the resulting texts. We find that, despite these interventions, classifiers reliably distinguish between resumes generated with male and female names. This leakage is not driven by overtly gendered wording, but by subtle differences in the usage of semantically equivalent, formally gender-neutral terms in German. In contrast, ethnicity-related leakage remains comparatively weak across models. Our findings demonstrate that apparently neutral resume generation can still preserve highly predictive demographic signals, raising concerns about anonymization-based fairness interventions in multilingual AI hiring pipelines.
Figures & tables
Figure 1: Overview of the two-stage audit pipeline. Stage 1 generates résumés from real anonymized job-matching profiles by systematically varying names as proxies for gender and ethnicity. Stage 2 assesses demographic leakage by anonymizing and gender-neutralizing the résumés before evaluating gender and ethnicity recoverability using TF-IDF representations and logistic regression classifiers.
Original word
Gender-neutral form
Beraterin ( female consultant )
→
Berater ( consultant )
Kaufmann ( salesman )
→
Kauf-mann/-frau ( salesperson )
Kauffrau ( saleswoman )
→
Kauf-mann/-frau ( salesperson )
Fachmann ( male specialist )
→
Fach-mann/-frau ( specialist )
Fachfrau ( female specialist )
→
Fach-mann/-frau ( specialist )
Table 1: Examples of gender-stem normalization.
Figure 2: Distribution of total résumé length (word count) across LLM families (left) and Qwen 3 model sizes (right).
Term
n (/30)
Mean
Mean selected
Performance
coefficient
features
Acc
AUC (sd)
ChatGPT
4.9
0.825
0.900 (0.046)
fachkraft specialist n.
30
-0.991 (f.)
fach-mann/-frau specialist m./f.
30
0.600 (m.)
Gemini
6.9
0.877
0.915 (0.034)
fachkraft specialist n.
30
-1.541 (f.)
Table 2: Most frequently selected TF–IDF lasso terms for gender prediction across 30 repeated grouped LASSO runs per model. Per term, we report the corresponding number of selected runs and mean coefficient across runs. Per model, we report mean selected features per run and mean classifier performance (Accuracy and AUC, with standard deviations (sd) over all runs).
Term
n (/30)
Mean
Mean selected
Performance
coefficient
features
Acc
AUC (sd)
ChatGPT
1.7
0.500
0.499 (0.005)
muttersprache mother tongue
16
-0.001 (t.)
Gemini
3.1
0.521
0.540 (0.027)
austausch exchange
24
-0.023 (t.)
Qwen 8B top-k = 20
1.6
0.500
0.503 (0.012)
Table 3: Most frequently selected TF–IDF lasso terms for ethnicity prediction across 30 repeated grouped LASSO runs per model. Per term, we report the corresponding number of selected runs and mean coefficient across runs. Per model, we report mean selected features per run and mean classifier performance (Accuracy and AUC, with standard deviations (sd) over all runs).
Target
Setting
4B
8B
14B
Acc
AUC
Acc
AUC
Acc
AUC
gender
top-k=20
0.636
0.683
0.670
0.754
0.687
0.686
top-k=64
0.616
0.652
0.654
0.704
0.670
0.679
ethnicity
top-k=20
0.500
0.499
0.500
0.503
0.566
0.579
top-k=64
0.519
0.529
0.500
0.500
0.542
0.557
Table 4: Leakage classifier performance across Qwen 3 model sizes and top-k decoding settings. Performance is averaged across 30 repeated runs.
Appendix figures & tables6 assets
Supplementary material from the paper’s appendix.
Appendix
Item Name
Type
Answers
Subanswers
Hard Skills
Industry Experience
MULTI
Open to all; Agency / Advertising / Marketing / PR; Plant and mechanical engineering / industry; …
Table 5: Overview of matching profile questionnaire items. Translated from German; illustrative selection of response options. Items are grouped into hard skills, soft skills, and preferences.
German
Turkish
Male
Female
Male
Female
Marcel Popp
Ramona Popp
Emre Eren
Havva Eren
Philipp Stoll
Mareen Stoll
Onur Ayyildiz
Kübra Ayyildiz
Kai Lindner
Carina Lindner
Orhan Turan
Cansu Turan
Fabian Kern
Britta Kern
Fikret Akman
Necla Akman
Marco Neubauer
Jennifer Neubauer
Yunus Sönmez
Gül Sönmez
Appendix
Table 6: Complete list of sampled names grouped by ethnicity and gender.
Figure 3: Distribution of total résumé length (word count) by gender across all model families. No systematic differences in résumé length are observed between male- and female-associated résumés.
Figure 4: Distribution of total résumé length (word count) by ethnicity across all model families. Résumé length remains broadly comparable between German- and Turkish-associated résumés, indicating no substantial ethnicity-related differences in output length.
Figure 5: Distribution of agentic word counts by gender across all model families.
Figure 6: Distribution of communal word counts by gender across all model families.
Algorithmic fairness studies how automated decisions distribute errors, opportunities, and harms across groups, and how to measure and reduce disparities.