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Achieving Fairness in Algorithmic Decision Making in HR
Manage episode 354749391 series 3428014
Sisällön tarjoaa Benoit Hardy-Vallée. Benoit Hardy-Vallée tai sen podcast-alustan kumppani lataa ja toimittaa kaiken podcast-sisällön, mukaan lukien jaksot, grafiikat ja podcast-kuvaukset. Jos uskot jonkun käyttävän tekijänoikeudella suojattua teostasi ilman lupaasi, voit seurata tässä https://fi.player.fm/legal kuvattua prosessia.
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.
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