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Resources

Data downloads, bibliography, and citation.

Data Downloads

Open data to enable independent verification and extended research.

Name Pairs

54 pairs across 6 categories.

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Symptom Profiles

20 standardized clinical presentations.

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Healthcare Findings

Effect sizes, confidence intervals, p-values.

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Structural Findings

12 dimensions. Results only.

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Bibliography

Complete references with DOIs.

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What Is Not Available

Structural analysis source code, detector implementations, raw model outputs, and full statistical analysis scripts are available only through research partnership.

Key Bibliography

Foundational research this work builds upon. Full bibliography available for download above.

Name-Based Discrimination

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
DOI: 10.1257/0002828042002561 →
Fryer, R. G., & Levitt, S. D. (2004)
The Causes and Consequences of Distinctively Black Names
Quarterly Journal of Economics, 119(3), 767-805
DOI: 10.1162/0033553041502180 →

Healthcare Disparities

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
DOI: 10.1073/pnas.1516047113 →
Schulman, K. A., et al. (1999)
The effect of race and sex on physicians' recommendations for cardiac catheterization
New England Journal of Medicine, 340(8), 618-626
DOI: 10.1056/NEJM199902253400806 →

Algorithmic Bias

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
DOI: 10.1126/science.aax2342 →
Buolamwini, J., & Gebru, T. (2018)
Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification
Proceedings of Machine Learning Research, 81, 77-91
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021)
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?
FAccT '21
DOI: 10.1145/3442188.3445922 →

How to Cite

Recommended Citation

DoAIKnowYou Research Initiative (2026). 
Nominative Bias in AI Systems: Evidence from Healthcare and Structural Analysis. 
https://doaiknowyou.com

BibTeX

@misc{doaiknowyou2026,
  title={Nominative Bias in AI Systems: Evidence from Healthcare and Structural Analysis},
  author={{DoAIKnowYou Research Initiative}},
  year={2026},
  url={https://doaiknowyou.com}
}