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Talks Tech #51: Fairness and Bias in Recommendation Systems
Manage episode 382391670 series 3292119
Contenuto fornito da Women Who Code. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da Women Who Code o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.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
252 episodi
Condividi
Manage episode 382391670 series 3292119
Contenuto fornito da Women Who Code. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da Women Who Code o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.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
252 episodi
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Show notes are at https://stevelitchfield.com/sshow/chat.html
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continue reading
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…
continue reading
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…
continue reading
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…
continue reading
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