CAMO: Explaining Consensus Across MOdels

Explainable AI methods have been proposed to help interpret complex models, e.g., by assigning importance scores to model features or perturbing the features in a way that changes the prediction. These methods apply to one model at a time, but in practice, engineers usually select from many candidate models and hyperparameters. To assist with this task, we demonstrate Camo:a tool that explains consensus among multiple models. Conference participants will interact with CAMO using a variety of models and datasets, to explore 1) consensus patterns, such as subsets of the test dataset or intervals within feature domains where models disagree, and 2) data perturbations that would make conflicting models agree (and consistent models disagree).