Addressing Algorithmic Unfairness in Healthcare
Artificial intelligence (AI) healthcare technology can solve our most complex and intransigent health issues. It has the potential to enhance healthcare quality, improve access, reduce cost, and deliver highly personalized care. However, delivering those solutions requires large, historical, broadly representative, and well-organized data from an affected population as well as from the individual that is seeking care. Without representative data and algorithms that render fair, accurate results, AI will exacerbate, rather than solve, longstanding health issues, especially for individuals that suffer from healthcare disparities.
Data Set Curation
The data sets that currently inform AI healthcare—upon which most new data sets are created—are frequently gathered from high-resourced, predominantly white communities, which may not be representative of treatment populations. Organizations with the goal of improving AI fairness by using representative data to create and improve algorithms, may use, combine, duplicate, or share individually identifiable health data collected from communities where greater data are needed, without their informed consent. For…
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