To make advanced learning machines such as Deep Neural Networks (DNNs) more transparent in decision making, explainable AI (XAI) aims to provide interpretations of DNNs' predictions. These interpretations are usually given in the form of heatmaps, each one illustrating relevant patterns regarding the prediction for a given instance. Bayesian approaches such as Bayesian Neural Networks (BNNs) so far have a limited form of transparency (model transparency) already built-in through their prior weight distribution, but notably, they lack explanations of their predictions for given instances. In this work, we bring together these two perspectives of transparency into a holistic explanation framework for explaining BNNs. Within the Bayesian framework, the network weights follow a probability distribution. Hence, the standard (deterministic) prediction strategy of DNNs extends in BNNs to a predictive distribution, and thus the standard explanation extends to an explanation distribution. Exploiting this view, we uncover that BNNs implicitly employ multiple heterogeneous prediction strategies. While some of these are inherited from standard DNNs, others are revealed to us by considering the inherent uncertainty in BNNs. Our quantitative and qualitative experiments on toy/benchmark data and real-world data from pathology show that the proposed approach of explaining BNNs can lead to more effective and insightful explanations.
翻译:为使深神经网络等先进的学习机器在决策中更加透明,可解释的AI(XAI)旨在解释DNN的预测。这些解释通常以热图的形式提供,每个说明特定情况下预测的相关模式。Bayesian神经网络(BNNS)等巴伊西亚方法迄今通过先前的重量分布已经具备了有限的透明度形式(模范透明度),但值得注意的是,它们缺乏对特定情况的预测的解释。在这项工作中,我们把这些透明度观点汇集到解释DNN的全方位解释框架之中。在Bayesian框架内,网络的权重随概率分布而变化。因此,DNNIS的标准(非定性)预测战略在BNIS中延伸至预测性分布,因此标准解释延伸到解释性分布。我们从这一观点中发现,BNNP的隐含多重混杂预测战略。虽然其中一些是标准DNNN的继承,但另一些则通过考虑BNNN的内在的不确定性和真实的GIL数据解释,向我们揭示了我们从BNNNN的定量和G的定量数据。