The classic computer science adage 'garbage in, garbage out' lacks nuance when it comes to understanding biased medical data, argue computer science and bioethics professors from MIT, Johns Hopkins University, and the Alan Turing Institute in a new opinion piece published in a recent edition of the New England Journal of Medicine (NEJM).
co-authored by Ghassemi's Ph.D. student Vinith Suriyakumar and University of California at San Diego Assistant Professor Berk Ustun, the researchers found that assuming the inclusion of personalized attributes like self-reported race improve the performance of ML models can actually lead to worse risk scores, models, and metrics for minority and minoritized populations.
"Generating high-quality, ethically sourced datasets is crucial for enabling the use of next-generation AI technologies that transform how we do research," NIH acting director Lawrence Tabak stated in a press release when the NIH announced its $130 million Bridge2AI Program last year. Elaine Nsoesie, an associate professor at the Boston University of Public Health, believes there are many potential benefits to treating biased datasets as artifacts rather than garbage, starting with the focus on context."Biases present in a dataset collected for lung cancer patients in a hospital in Uganda might be different from a dataset collected in the U.S. for the same patient population," she explains.
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