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Strategic Classification

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When machine learning algorithms are deployed, we often assume their predictions do not affect the outcomes they are trained to forecast. However, this principle can break down when welfare (employment, education, health) of rational individuals is affected. Knowing information about the algorithm, such individuals may manipulate their attributes in order to obtain a personally beneficial prediction (e.g., a higher credit score rating).

This reading group focuses on whether and how we can design machine learning algorithms which achieve non-trivial performance even in presence of strategic individuals. We further explore the potentially negative social effects of strategic classification, and the broader phenomenon of performativity of which strategic classification is a special case.

This talk is part of the Machine Learning Reading Group @ CUED series.

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