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Can AI Improve Health Without Perpetuating Bias?

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Content provided by The Commonwealth Fund. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by The Commonwealth Fund or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ro.player.fm/legal.

On this week’s episode of The Dose, host Joel Bervell speaks with Dr. Ziad Obermeyer, from the University of California Berkeley’s School of Public Health, about the potential of AI in informing health outcomes — for better and for worse.

Obermeyer is the author of groundbreaking research on algorithms, which are used on a massive scale in health care systems — for instance, to predict who is likely to get sick and then direct resources to those populations. But they can also entrench racism and inequality into the system.

“We've accumulated so much data in our electronic medical records, in our insurance claims, in lots of other parts of society, and that’s really powerful,” Obermeyer says. “But if we aren’t super careful in what lessons we learn from that history, we’re going to teach algorithms bad lessons, too.”

Citations

Dr. Ziad Obermeyer

Dissecting racial bias in an algorithm used to manage the health of populations

Nightingale Open Science

  continue reading

106 episoade

Artwork
iconDistribuie
 
Manage episode 360657592 series 2463238
Content provided by The Commonwealth Fund. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by The Commonwealth Fund or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ro.player.fm/legal.

On this week’s episode of The Dose, host Joel Bervell speaks with Dr. Ziad Obermeyer, from the University of California Berkeley’s School of Public Health, about the potential of AI in informing health outcomes — for better and for worse.

Obermeyer is the author of groundbreaking research on algorithms, which are used on a massive scale in health care systems — for instance, to predict who is likely to get sick and then direct resources to those populations. But they can also entrench racism and inequality into the system.

“We've accumulated so much data in our electronic medical records, in our insurance claims, in lots of other parts of society, and that’s really powerful,” Obermeyer says. “But if we aren’t super careful in what lessons we learn from that history, we’re going to teach algorithms bad lessons, too.”

Citations

Dr. Ziad Obermeyer

Dissecting racial bias in an algorithm used to manage the health of populations

Nightingale Open Science

  continue reading

106 episoade

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