Abstract
In recent years, Artificial Intelligence (AI) models have surged in popularity and success, garnering much attention across many disciplines and applications. In particular, recent developments in Deep Neural Networks (DNN) have allowed them to match and even surpass human capabilities in many tasks. However, the complexity of deep neural networks causes them function like black boxes that are very difficult for humans to understand, thus making them untrustworthy and limiting their usefulness in high-risk applications. Explainable Artificial Intelligence aims to alleviate this issue by generating explanations for DNNs to allow humans to understand their reasoning and increase their transparency. This paper reviews research on neural network explainability, detailing its definition, necessity, classification, and evaluation. It categorizes current explanation methods for deep neural networks according to their number of inputs. Additionally, it introduces the principles and evaluation methods for common interpretability algorithms. Future research directions and applications of interpretable neural networks are summarized. The challenges facing interpretable neural networks are outlined, along with potential solutions to these challenges.
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