As more machine learning tools reach patients, developers are starting to get smart about the potential for bias to seep in. But a growing body of research aims to emphasize that even carefully trained models — ones built to ignore race — can breed inequity in care.
Researchers at the Massachusetts Institute of Technology and IBM Research recently showed that algorithms based on clinical notes — the free-form text providers jot down during patient visits — could predict the self-identified race of a patient, even when the data had been stripped of explicit mentions of race. It’s a clear sign of a big problem: Race is so deeply embedded in clinical information that straightforward approaches like race redaction won’t cut it when it comes to making sure algorithms aren’t biased.
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