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Achieving Fairness in Algorithmic Decision Making in HR
Manage episode 354749391 series 3428014
Το περιεχόμενο παρέχεται από το Benoit Hardy-Vallée. Όλο το περιεχόμενο podcast, συμπεριλαμβανομένων των επεισοδίων, των γραφικών και των περιγραφών podcast, μεταφορτώνεται και παρέχεται απευθείας από τον Benoit Hardy-Vallée ή τον συνεργάτη της πλατφόρμας podcast. Εάν πιστεύετε ότι κάποιος χρησιμοποιεί το έργο σας που προστατεύεται από πνευματικά δικαιώματα χωρίς την άδειά σας, μπορείτε να ακολουθήσετε τη διαδικασία που περιγράφεται εδώ https://el.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.
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