Arbitrariness Lies Beyond the Fairness-Accuracy Frontier
Carol Xuan Long Hsiang Hsu Wael Alghamdi Flavio P. Calmon
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. Consequently, state-of-the-art
fairness interventions can mask high predictive multiplicity behind favorable
group fairness and accuracy metrics. 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.