# Are phenotype algorithms fair for underrepresented minorities within older adults?- Revision

> **NIH NIH P30** · STANFORD UNIVERSITY · 2020 · $50,916

## Abstract

Are phenotyping algorithms fair for underrepresented minorities within older adults?
According to the latest population estimates by the US Census bureau, the United States
population is growing older and more diverse, with their projections indicating that in 2035 there
will be more people 65-and-older than 18-and-younger. In terms of diversity, the US Census
Bureau projects that by 2060 the non-Hispanic White-alone population will shrink by 20 million,
leading to the increased representation of currently underrepresented minority groups. These
future population milestones paired with the rise of artificial intelligence (AI) use in medicine,
present a very unique and problematic problem: determining if AI solutions are fair for
underrepresented populations within older adults. This has been a very debated issue, with
opinion pieces in the New York times making it clear that when done incorrectly, AI solutions
can actually worsen health disparities in underrepresented populations due to the nature of the
training sets for said AI solutions. It is intuitive to understand how dermatology AI systems could
be biased to perform better for lighter skinned individuals, due to the prevalence of said patients
in the training datasets. Other biases come from the fact that there is simply a lack of diverse
research subjects, which is why their mortality outcomes are considerably worse. Electronic
phenotyping algorithms are the cornerstone for automatic identification of selected patient
groups for tasks like disease classification, epidemiological studies and clinical-trial recruitment.
These algorithms are both rule-based or probabilistic in nature (machine learning models), and
are usually built by using bundled patient populations (everybody put together), with very few
exceptions (due to the nature of the phenotype) having some sort of stratification of populations.
This proposed project seeks to identify bias in probabilistic electronic phenotype algorithms for
older populations and create best-practices and software tools to overcome them in order to
lead to better health outcomes.

## Key facts

- **NIH application ID:** 10049865
- **Project number:** 3P30AG059307-02S1
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Victor Henderson
- **Activity code:** P30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $50,916
- **Award type:** 3
- **Project period:** 2018-09-30 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10049865

## Citation

> US National Institutes of Health, RePORTER application 10049865, Are phenotype algorithms fair for underrepresented minorities within older adults?- Revision (3P30AG059307-02S1). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10049865. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*