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Talks Tech #51: Fairness and Bias in Recommendation Systems

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Content provided by Women Who Code. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Women Who Code 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.

Welcome to the Women Who Code podcast. I am Ashmi Banerjee, a PhD candidate at the Technical University of Munich specializing in Recommended Systems Research. Today, we will explore the topic of fairness and biases in recommended systems.
Episode: https://www.womenwhocode.com/blog/excelling-as-a-black-woman-in-leadership/
Video: https://www.youtube.com/watch?v=1XBa_zpJTf4
Guest: Ashmi Banerjee, a PhD candidate at the Technical University of Munich specializing in Recommended Systems Research

  continue reading

252 episoade

Artwork
iconDistribuie
 
Manage episode 382391670 series 3292119
Content provided by Women Who Code. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Women Who Code 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.

Welcome to the Women Who Code podcast. I am Ashmi Banerjee, a PhD candidate at the Technical University of Munich specializing in Recommended Systems Research. Today, we will explore the topic of fairness and biases in recommended systems.
Episode: https://www.womenwhocode.com/blog/excelling-as-a-black-woman-in-leadership/
Video: https://www.youtube.com/watch?v=1XBa_zpJTf4
Guest: Ashmi Banerjee, a PhD candidate at the Technical University of Munich specializing in Recommended Systems Research

  continue reading

252 episoade

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