Individual Arbitrariness and Group Fairness

Machine learning tasks may admit multiple competing models that achieve similar performance yet produce conflicting outputs for individual samples---a phenomenon known as predictive multiplicity. We demonstrate that fairness interventions in machine learning optimized solely for group fairness and accuracy can exacerbate predictive multiplicity. We argue that a third axis of arbitrariness'' should be considered when deploying models to aid decision-making in applications of individual-level impact.To address this challenge, we propose an ensemble algorithm applicable to any fairness intervention that provably ensures more consistent predictions.