COPA: Comparing the incomparable in multi-objective model evaluation

COPA, a new method for multi-objective model evaluation in machine learning, was introduced in a recent arXiv publication. This method addresses the complexities of comparing diverse objectives such as accuracy, robustness, fairness, and scalability, which are often measured in different units. By normalizing and aggregating these objectives, COPA enables practitioners to navigate the Pareto front systematically, aligning with user-specific preferences. Its potential impact spans various areas, including fair ML, domain generalization, AutoML, and foundation models, where traditional methods have struggled. The introduction of COPA is significant as it streamlines the model selection process, making it less time-consuming and more accessible to users without extensive expertise.