In order to facilitate algorithmic development with this broader context, we have released ML-fairness-gym, a set of components for building simple simulations that explore potential long-run impacts of deploying machine learning-based decision systems in social environments. In “Fairness is not Static: Deeper Understanding of Long Term Fairness via Simulation Studies” we demonstrate how the ML-fairness-gym can be used to research the long-term effects of automated decision systems on a number of established problems from current machine learning fairness literature.
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