The accelerated progress of artificial intelligence (AI) has popularized deep
learning models across domains, yet their inherent opacity poses challenges,
notably in critical fields like healthcare, medicine and the geosciences.
Explainable AI (XAI) has emerged to shed light on these "black box" models,
helping decipher their decision making process. Nevertheless, different XAI
methods yield highly different explanations. This inter-method variability
increases uncertainty and lowers trust in deep networks' predictions. In this
study, for the first time, we propose a novel framework designed to enhance the
explainability of deep networks, by maximizing both the accuracy and the
comprehensibility of the explanations. Our framework integrates various
explanations from established XAI methods and employs a non-linear "explanation
optimizer" to construct a unique and optimal explanation. Through experiments
on multi-class and binary classification tasks in 2D object and 3D neuroscience
imaging, we validate the efficacy of our approach. Our explanation optimizer
achieved superior faithfulness scores, averaging 155% and 63% higher than the
best performing XAI method in the 3D and 2D applications, respectively.
Additionally, our approach yielded lower complexity, increasing
comprehensibility. Our results suggest that optimal explanations based on
specific criteria are derivable and address the issue of inter-method
variability in the current XAI literature.

通过整合不同的解释方法并利用非线性的 “解释优化器”，我们提出了一种新颖的框架，旨在增强深度神经网络的可解释性，并通过实验证实了其有效性。