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\\ ==== Next talk ====
[[en:seminaires:irif:index|IRIF Distinguished Talks Series]]\\
Tuesday May 14, 2024, 11AM, TBA\\
**Omer Reingold** (Stanford) //The multitude of group affiliations: Algorithmic Fairness, Loss Minimization and Outcome Indistinguishability//
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We will survey a rather recent and very fruitful line of research in algorithmic fairness, coined multi-group fairness. We will focus on risk prediction, where a machine learning algorithm tries to learn a predictor to answer questions of the form “what is the probability that patient x will experience a particular medical condition?” Training a risk predictor to minimize a loss function fixed in advance is the dominant paradigm in machine learning. However, global loss minimization may create predictions that are mis-calibrated on sub-populations, causing harm to individuals of these populations. Multi-group fairness tries to prevent forms of discrimination to a rich (possibly exponential) collection of arbitrarily intersecting groups. In a sense, it provides a computational perspective on the meaning of individual risks and the classical tension between clinical prediction, which uses individual-level traits, and actuarial prediction, which uses group-level traits.

While motivated in fairness, this alternative paradigm for training an indistinguishable predictor is finding a growing number of appealing applications, where the same predictor can later be used to optimize one of a large set of loss functions, under a family of capacity and fairness constraints and instance distributions.

Based on a sequence of works joint with (subsets of) Cynthia Dwork, Shafi Goldwasser, Parikshit Gopalan, Úrsula Hébert-Johnson, Lunjia Hu, Adam Kalai, Christoph Kern, Michael P. Kim, Frauke Kreuter, Guy N. Rothblum, Vatsal Sharan, Udi Wieder, Gal Yona and others.
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