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Bias in machine learning for healthcare with Marzyeh Ghassemi (University of Toronto)

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Manage episode 270894227 series 2769784
Content provided by Anika Gupta. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Anika Gupta 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://ppacc.player.fm/legal.

Humans tend towards bias, but are our algorithms objective? Today I discuss fairness and bias in machine learning for healthcare with Professor Maryzeh Ghassemi of the University of Toronto. We delve into the ways in which bias pops up in the data that are used to train computational models, the particular dangers of systemic inequalities in healthcare being perpetuated by algorithms, and some of the steps needed to combat these deeply rooted issues.

Check out the glossary of terms, definitions, and resources (and get a sneak peak of the future conversations lined up!) here: bit.ly/datapulse-glossary

--- Support this podcast: https://podcasters.spotify.com/pod/show/the-data-pulse/support
  continue reading

25 episodes

Artwork
iconShare
 
Manage episode 270894227 series 2769784
Content provided by Anika Gupta. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Anika Gupta 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://ppacc.player.fm/legal.

Humans tend towards bias, but are our algorithms objective? Today I discuss fairness and bias in machine learning for healthcare with Professor Maryzeh Ghassemi of the University of Toronto. We delve into the ways in which bias pops up in the data that are used to train computational models, the particular dangers of systemic inequalities in healthcare being perpetuated by algorithms, and some of the steps needed to combat these deeply rooted issues.

Check out the glossary of terms, definitions, and resources (and get a sneak peak of the future conversations lined up!) here: bit.ly/datapulse-glossary

--- Support this podcast: https://podcasters.spotify.com/pod/show/the-data-pulse/support
  continue reading

25 episodes

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