Finding the Right XAI Method—A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Science

Abstract Explainable artificial intelligence (XAI) methods shed light on the predictions of machine learning algorithms. Several different approaches exist and have already been applied in climate science. However, usually missing ground truth explanations complicate their evaluation and comparison, subsequently impeding the choice of the XAI method. Therefore, in this work, we introduce XAI evaluation in the climate context and discuss different desired explanation properties, namely, robustness, faithfulness, randomization, complexity, and localization. To this end, we chose previous work as a case study where the decade of annual-mean temperature maps is predicted. After training both a multilayer perceptron (MLP) and a convolutional neural network (CNN), multiple XAI methods are applied and their skill scores in reference to a random uniform explanation are calculated for each property. Independent of the network, we find that XAI methods such as Integrated Gradients, layerwise relevance propagation, and input times gradients exhibit considerable robustness, faithfulness, and complexity while sacrificing randomization performance. Sensitivity methods, gradient, SmoothGrad, NoiseGrad, and FusionGrad, match the robustness skill but sacrifice faithfulness and complexity for the randomization skill. We find architecture-dependent performance differences regarding robustness, complexity, and localization skills of different XAI methods, highlighting the necessity for research task-specific evaluation. Overall, our work offers an overview of different evaluation properties in the climate science context and shows how to compare and benchmark different explanation methods, assessing their suitability based on strengths and weaknesses, for the specific research problem at hand. By that, we aim to support climate researchers in the selection of a suitable XAI method. Significance Statement Explainable artificial intelligence (XAI) helps to understand the reasoning behind the prediction of a neural network. XAI methods have been applied in climate science to validate networks and provide new insight into physical processes. However, the increasing number of XAI methods can overwhelm practitioners, making it difficult to choose an explanation method. Since XAI methods’ results can vary, uninformed choices might cause misleading conclusions about the network decision. In this work, we introduce XAI evaluation to compare and assess the performance of explanation methods based on five desirable properties. We demonstrate that XAI evaluation reveals the strengths and weaknesses of different XAI methods. Thus, our work provides climate researchers with the tools to compare, analyze, and subsequently choose explanation methods.