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Workshop: Algorithmic Fairness through the Lens of Time

Learning in reverse causal strategic environments with ramifications on two sided markets

Seamus Somerstep · Yuekai Sun · Ya'acov Ritov

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[ Poster
presentation: Algorithmic Fairness through the Lens of Time
Fri 15 Dec 7 a.m. PST — 3:30 p.m. PST


Motivated by equilibrium models of labor markets, we develop a formulation of causal strategic classification in which strategic agents can directly manipulate their outcomes. As an application, we consider employers that seeks to anticipate the strategic response of labor force when developing a hiring policy. We show theoretically that such performative (optimal) hiring policies improves employer and labor force welfare (compared to employers that do not anticipate the strategic labor force response) in the classic Coate-Loury labor market model. Empirically, we show that these desirable properties of performative hiring policies do generalize to our own formulation of a general equilibrium labor market. On the other hand, we observe that in our formulation a performative firm both harms workers by reducing their aggregate utility and fails to prevent discrimination when more sophisticated wage and cost structures are introduced.

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