Detecting, Characterizing, and Mitigating Implicit and Explicit Racial Biases in Health Care Datasets With Subgroup Learnability: Algorithm Development and Validation Study
In short, we demonstrated that AEquity is a robust tool by applying it to different datasets, algorithms, and intersectional analyses and measuring its effectiveness with respect to a range of traditional fairness metrics.
Authors
- Faris F. Gulamali (ORCID: https://orcid.org/0000-0002-2973-6594)
- Karandeep Singh (ORCID: https://orcid.org/0000-0001-8980-2330)
- Ashwin Sawant (ORCID: https://orcid.org/0000-0003-1525-8541)
- Emmanuel Mensah (ORCID: https://orcid.org/0000-0002-7512-9525)
- Jianying Hu (ORCID: https://orcid.org/0000-0001-7753-886X)
- Girish N. Nadkarni (ORCID: https://orcid.org/0000-0001-6319-4314)
- Lili Chan (ORCID: https://orcid.org/0000-0003-4300-5760)
- Carol R. Horowitz (ORCID: https://orcid.org/0000-0003-1517-4700)
- David L. Reich (ORCID: https://orcid.org/0000-0003-0095-515X)
- Lynne D. Richardson (ORCID: https://orcid.org/0000-0002-5425-1601)
- Lora E. Liharska (ORCID: https://orcid.org/0009-0001-1728-683X)
- Ira S. Hofer (ORCID: https://orcid.org/0000-0001-7456-6697)
- Alexander W. Charney (ORCID: https://orcid.org/0000-0001-8135-6858)
Institutions
- Christiana Care Health System (US)
- University of California San Diego (US)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2025-04-04
- DOI
- https://doi.org/10.2196/71757
- Citations
- 2
- Primary Topic
- Climate Change and Health Impacts
- Type
- article
- Field-Weighted Citation Impact
- 2.40