))
Achieving Fairness in Algorithmic Decision Making in HR
Manage episode 354749391 series 3428014
Contenido proporcionado por Benoit Hardy-Vallée. Todo el contenido del podcast, incluidos episodios, gráficos y descripciones de podcast, lo carga y proporciona directamente Benoit Hardy-Vallée 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.
Join us on this episode as we dive into the complex world of algorithmic fairness in HR with Manish Raghavan, Assistant Professor of Information Technology at the MIT Sloan School of Management. Discover the challenges and opportunities of using algorithms to make decisions about people, and learn about the importance of preventing algorithms from replicating discriminatory and unfair human decision-making. Get insights into the distinction between procedural fairness and outcome fairness, and understand why the deployment environment of a machine learning model is just as crucial as the technology itself. Gain a deeper understanding of the scoring mechanism behind algorithmic tools, and the potential dangers and consequences of their use. Learn how common signals in assessments can result in similar assessments across organizations and what it takes to achieve fairness in algorithmic decision-making in HR.
Manish page at MIT
Follow Manish on LinkedIn
- More content at abruptfuture.com
- Connect with Benoit on LinkedIn
- Follow our page on LinkedIn here
42 episodios
Compartir
