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Talks Tech #51: Fairness and Bias in Recommendation Systems
Manage episode 382391670 series 3292119
Contenido proporcionado por Women Who Code. Todo el contenido del podcast, incluidos episodios, gráficos y descripciones de podcast, lo carga y proporciona directamente Women Who Code o su socio de plataforma de podcast. Si cree que alguien está utilizando su trabajo protegido por derechos de autor sin su permiso, puede seguir el proceso descrito aquí https://es.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 episodios
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Manage episode 382391670 series 3292119
Contenido proporcionado por Women Who Code. Todo el contenido del podcast, incluidos episodios, gráficos y descripciones de podcast, lo carga y proporciona directamente Women Who Code o su socio de plataforma de podcast. Si cree que alguien está utilizando su trabajo protegido por derechos de autor sin su permiso, puede seguir el proceso descrito aquí https://es.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 episodios
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continue reading
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