Partial order: Finding Consensus among Uncertain Feature Attributions

Post-hoc feature importance is progressively being employed to explain decisions of complex machine learning models. Yet in practice, reruns of the training algorithm and/or the explainer can result in contradicting statements of feature importance, henceforth reducing trust in those techniques. A possible avenue to address this issue is to develop strategies to aggregate diverse explanations about feature importance. While the arithmetic mean, which yields a total order, has been advanced, we introduce an alternative: the consensus among multiple models, which results in partial orders. The two aggregation strategies are compared using Integrated Gradients and Shapley values on two regression datasets, and we show that a large portion of the information provided by the mean aggregation is not supported by the consensus of each individual model, raising suspicion on the trustworthiness of this practice.