We show how decision-focused learning can improve the fairness-accuracy tradeoff in algorithmic decision-making using a case study on a real-world domain.
We create a visualization to allow various stakeholders to evaluate the tradeoffs associated with multi-party fairness in the Ride-Pooling (Uber) ecosystem.
We evaluate the efficacy of two different strategies of enforcing fairness in ride-pooling.