Explainability of AI models is an important topic that can have a significant impact in all domains and applications from autonomous driving to healthcare. The existing approaches to explainable AI (XAI) are mainly limited to simple machine learning algorithms, and the research regarding the explainability-accuracy tradeoff is still in its infancy especially when we are concerned about complex machine learning techniques like neural networks and deep learning (DL). In this work, we introduce a new approach for complex models based on the co-relation impact which enhances the explainability considerably while also ensuring the accuracy at a high level. We propose approaches for both scenarios of independent features and dependent features. In addition, we study the uncertainty associated with features and output. Furthermore, we provide an upper bound of the computation complexity of our proposed approach for the dependent features. The complexity bound depends on the order of logarithmic of the number of observations which provides a reliable result considering the higher dimension of dependent feature space with a smaller number of observations.

翻译：人工智能模型的可解释性是一个重要议题，从自动驾驶到医疗保健等所有领域和应用中都能产生重大影响。现有的可解释人工智能方法主要局限于简单的机器学习算法，而关于可解释性-准确性权衡的研究仍处于起步阶段，尤其是当我们关注神经网络和深度学习等复杂机器学习技术时。在本研究中，我们提出了一种基于相关影响的新方法用于复杂模型，该方法在显著增强可解释性的同时，也能确保高水平的准确性。我们针对独立特征和依赖特征两种场景分别提出了相应的方法。此外，我们研究了与特征和输出相关的不确定性。进一步地，我们给出了所提方法在依赖特征情况下的计算复杂度上界。该复杂度上界取决于观测数量的对数阶，从而在依赖特征空间维度较高、观测数量较少的情况下提供了可靠的结果。