Finding the right XAI method – A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Science
Artificial Intelligence for the Earth Systems(2023)
摘要
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 multi-layer 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 Integrated Gradients, layer-wise
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 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.
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