Research Resources

Everything you need to understand, replicate, or extend our research.

Pre-Registered Research

This study's methodology and analysis plan were specified before data collection began, preventing post-hoc adjustment of methods to achieve desired results.

Protocol version 1.0 — January 2026

Data Downloads

All datasets in JSON format for maximum accessibility.

Name Pairs Dataset

54 name pairs across 6 demographic contrast categories with length matching and category labels.

JSON 11 KB 54 pairs
Download JSON

Symptom Profiles

20 standardized symptom profiles covering cardiovascular, neurological, abdominal, respiratory, pain, mental health, and fatigue categories.

JSON 10 KB 20 profiles
Download JSON

Pain Vignettes

5 clinical reasoning vignettes for pain management scenarios with demographic variants.

JSON 6 KB 5 vignettes
Download JSON

Cardiac Vignettes

5 clinical reasoning vignettes for cardiac scenarios including STEMI, atypical chest pain, and heart failure.

JSON 6 KB 5 vignettes
Download JSON

Psychiatric Vignettes

5 clinical reasoning vignettes for psychiatric scenarios including psychosis, depression, and anxiety.

JSON 6 KB 5 vignettes
Download JSON

Emergency Vignettes

5 clinical reasoning vignettes for emergency scenarios including acute abdomen and mental health crisis.

JSON 6 KB 5 vignettes
Download JSON

Key Literature

Foundational research our methodology builds upon.

Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019)

Dissecting racial bias in an algorithm used to manage the health of populations

Science, 366(6464), 447-453

Relevance: Demonstrated racial bias in a healthcare algorithm affecting millions of patients. Foundational methodology for algorithmic fairness research.
DOI: 10.1126/science.aax2342 →

Hoffman, K. M., Trawalter, S., Axt, J. R., & Oliver, M. N. (2016)

Racial bias in pain assessment and treatment recommendations, and false beliefs about biological differences between blacks and whites

PNAS, 113(16), 4296-4301

Relevance: Documented systematic racial bias in pain assessment. Our pain management findings mirror these human biases.
DOI: 10.1073/pnas.1516047113 →

Schulman, K. A., et al. (1999)

The effect of race and sex on physicians' recommendations for cardiac catheterization

NEJM, 340(8), 618-626

Relevance: Classic study showing disparities in cardiac referrals. Our cardiac vignette findings show similar patterns in AI systems.
DOI: 10.1056/NEJM199902253400806 →

Bertrand, M., & Mullainathan, S. (2004)

Are Emily and Greg More Employable Than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination

American Economic Review, 94(4), 991-1013

Relevance: Established the matched-pair testing methodology using names as demographic signals that our research extends to healthcare AI.
DOI: 10.1257/0002828042002561 →

Strakowski, S. M., et al. (2003)

The impact of race on diagnosis and disposition from a psychiatric emergency service

Journal of Clinical Psychiatry, 64(4), 395-401

Relevance: Documented racial disparities in psychiatric diagnosis (schizophrenia vs. bipolar). Our psychiatric findings parallel this pattern.

Citation

How to Cite This Research

Healthcare AI Fairness Observatory (2026). Name-Based Discrimination in Consumer Medical AI Symptom Checkers: A Matched-Pair Experimental Study. https://aifairnesslab.org
Additional Formats

License

Code

MIT License

Free to use, modify, and distribute. Include original license and copyright notice.

Data

CC-BY-4.0

Free to share and adapt. Attribution required.

Documentation

CC-BY-4.0

Free to share and adapt. Attribution required.

Questions?

If you have questions about the research, data, or resources, please reach out.

Our Ethics Press Kit Full Methodology