Explainability is needed to establish confidence in machine learning results. Some explainable methods take a post hoc approach to explain the weights of machine learning models, others highlight areas of the input contributing to decisions. These methods do not adequately explain decisions, in plain terms. Explainable property-based systems have been shown to provide explanations in plain terms, however, they have not performed as well as leading unexplainable machine learning methods. This research focuses on the importance of metrics to explainability and contributes two methods yielding performance gains. The first method introduces a combination of explainable and unexplainable flows, proposing a metric to characterize explainability of a decision. The second method compares classic metrics for estimating the effectiveness of neural networks in the system, posing a new metric as the leading performer. Results from the new methods and examples from handwritten datasets are presented.

翻译：为建立对机器学习结果的信任，可解释性至关重要。部分可解释方法采用事后分析来解释机器学习模型的权重参数，另一些方法则通过高亮输入中对决策贡献显著的区域来实现解释。然而，这些方法均未能以通俗易懂的方式充分阐释决策过程。基于可解释属性的系统已被证明能够提供通俗的解释，但其性能尚未达到领先的不可解释机器学习方法的水平。本研究聚焦于评估指标对可解释性的重要性，并提出了两种能提升性能的方法。第一种方法引入了可解释流程与不可解释流程的融合机制，并提出了一种用于量化决策可解释程度的评估指标。第二种方法通过比较评估神经网络在系统中有效性的经典指标，提出了一种表现优异的新评估指标。本文展示了新方法的实验结果及手写数据集上的应用案例。