Intersectional Bias in Language Models

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LLM-as-a-Demographic: Whom Sociodemographic Prompting Helps, and Whom It Hurts
Aug 31, 2026Daniela Occhipinti, Andrea Piergentili, Marco GueriniLLM-as-a-Judge Intersectional Bias in Language Models
Fondazione Bruno Kessler, Via Sommarive 18, Povo, Trento, Italy · Almawave Labs, Via Di Casal Boccone, 188/190, Rome
Calibrated? Not for Everyone: How Sexual Orientation and Religious Markers Distort LLM Accuracy and Confidence in Medical QA
Apr 19, 2026Alberto Testoni, Iacer CalixtoIntersectional Bias in Language Models Healthcare
Department of Medical Informatics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands. · Amsterdam Public Health, Methodology, Amsterdam, The Netherlands.
Intersectional Sycophancy: How Perceived User Demographics Shape False Validation in Large Language Models
Apr 13, 2026Benjamin Maltbie, Shivam RavalIntersectional Bias in Language Models LLM Sycophancy
Massachusetts Institute of Technology, Cambridge, MA, USA · Harvard University, Cambridge, MA, USA
From Seeing it to Experiencing it: Interactive Evaluation of Intersectional Voice Bias in Human-AI Speech Interaction
Mar 19, 2026Shree Harsha Bokkahalli Satish, Maria Teleki, Christoph Minixhofer +3Intersectional Bias in Language Models Language Model Bias Evaluation
KTH Royal Institute of Technology Stockholm, Sweden · Texas A&M University College Station, USA · University of Edinburgh Edinburgh, UK
Invisible Influences: Investigating Implicit Intersectional Biases through Persona Engineering in Large Language Models
Mar 16, 2026Nandini Arimanda, Achyuth Mukund, Sakthi Balan Muthiah +1Intersectional Bias in Language Models Social Bias in Language Models
Department of Computer Science and Engineering, Shiv Nadar University Chennai, Tamil Nadu, India · School of AI and CS, Plaksha University, Mohali, Punjab, India
The Voice Behind the Words: Quantifying Intersectional Bias in SpeechLLMs
Mar 15, 2026Shree Harsha Bokkahalli Satish, Christoph Minixhofer, Maria Teleki +5Intersectional Bias in Language Models Language Model Bias Evaluation
Department of Speech, Music and Hearing, KTH Royal Institute of Technology, Sweden · Centre for Speech Technology Research, University of Edinburgh, UK · Texas A&M University, USA
Investigating Gender Stereotypes in Large Language Models via Social Determinants of Health
Mar 10, 2026Trung Hieu Ngo, Adrien Bazoge, Solen Quiniou +2Intersectional Bias in Language Models Social Bias in Language Models
Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004, F-44000 Nantes, France · Nantes Université, CHU Nantes, Clinique des données, INSERM, CIC 1413, Nantes, France
Towards a more efficient bias detection in financial language models
Mar 9, 2026Firas Hadj Kacem, Ahmed Khanfir, Mike PapadakisFinancial Language Model Evaluation Intersectional Bias in Language Models
SnT, University of Luxembourg, Luxembourg · RIADI, ENSI, University of Manouba, Tunisia
ABLEIST: Intersectional Disability Bias in LLM-Generated Hiring Scenarios
Oct 13, 2025Mahika Phutane, Hayoung Jung, Matthew Kim +2Intersectional Bias in Language Models Social Bias in Language Models
Cornell University · Princeton University · University of Washington

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LLM-as-a-Demographic: Whom Sociodemographic Prompting Helps, and Whom It Hurts

Daniela Occhipinti, Andrea Piergentili, Marco Guerini

Fondazione Bruno Kessler, Via Sommarive 18, Povo, Trento, Italy · Almawave Labs, Via Di Casal Boccone, 188/190, Rome

LLM-as-a-Judge Intersectional Bias in Language Models LLM Prompting Social Bias in Language Models Language Model Bias Evaluation Bias in LLM-as-a-Judge+1

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Large language models (LLMs) are increasingly used as judges for subjective tasks, where annotators disagree and the relevant question is not only how accurate a judge is, but whose judgments it reproduces. Sociodemographic prompting conditions the judge on an annotator's demographic profile to align its judgments with the corresponding group's. We test whether this alignment emerges distributionally, comparing the predicted label distributions of 23 open-weight LLMs on three subjective tasks against those of real annotator groups, under three conditions: no demographic information, single-attribute profiles, and intersectional profiles over gender, age, race, and education. Three findings emerge. First, a judge prompted with no demographics is not perspective-neutral: models best reproduce the judgments of White, college-educated annotators. Second, demographic conditioning is asymmetric: it moves the judge toward majority groups and away from minority groups, most strongly on offensiveness, where intersectional profiles amplify the harm. Third, by comparing base and instruct models we identify instruction-tuning as a possible source of the asymmetry. Demographic conditioning should therefore be used with caution to estimate group judgments: conditioning moves predictions away from the reference distributions of the minority groups the method is often invoked to serve.