Medical biases need to be addressed before machine learning can improve health care

Assistant Professor Marzyeh Ghassemi spoke to Steve Nadis from MIT CSAIL about her research into machine learning in the health care sector. But after completing her PhD work, she came to a realisation via one of her committee members:

“It wasn’t until the end of my PhD work that one of my committee members asked: ‘Did you ever check to see how well your model worked across different groups of people?'”

Ghassemi eventually did and found that there were some serious disparities.

Upon a closer look, she saw that models often worked differently — specifically worse — for populations including Black women, a revelation that took her by surprise. “I hadn’t made the connection beforehand that health disparities would translate directly to model disparities,” she says. “And given that I am a visible minority woman-identifying computer scientist at MIT, I am reasonably certain that many others weren’t aware of this either.”

You can read her paper (co-written with Dr Elaine Nsoesie), In medicine, how do we machine learn anything real?’, in Patterns.

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