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CDH Seminar with Dr. Leo Celi "Better than Humans: Building AI that is less prejudiced, more fair"
The biggest threat to the adoption of artificial intelligence (AI) in healthcare is the concern that training algorithms on real world data will encrypt societal, institutional and individual biases, legitimize them and propagate them at scale. At present, the evaluation metric for machine learning in healthcare is accuracy. But just because an algorithm is accurate does not mean it should be implemented. If all that matters is accuracy, then algorithms developed using real-world data will encrypt the biases and prejudice that taint clinical decision-making. In an ideal world, only patient health and disease factors would determine — and guide the prediction of — clinical outcomes. However, studies have repeatedly demonstrated that this is far from the case. Women with heart attacks have worse outcomes when cared for by male cardiologists. Black newborns have better outcomes when their pediatricians are Black. Outcomes from sepsis are worse in hospitals that disproportionately treat minority patients after adjusting for illness severity and other confounders. To prevent AI from encoding social and cultural biases, we would like to predict an outcome if the world were fair, and the quality of care is the same across populations. We need algorithms that are better than humans - less prejudiced and more fair. 

Please note that this virtual event, including attendees’ Zoom video, audio and screen name, and questions or chats, will be recorded. All or portions of the event recording may be shared through the Center for Digital Health’s digital channels. Individuals who do not want their identities to be captured are solely responsible for turning off their camera, muting their microphone and/or adjusting their screen name accordingly. By attending this event, you consent to your name, voice, and/or image being recorded and to CDH reproducing, distributing and otherwise displaying the recording, within its sole discretion.

Jan 19, 2022 01:00 PM in Eastern Time (US and Canada)

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