Explaining the Unexplained: Revealing Hidden Correlations for Better Interpretability

Thanks to the rapid advancement of computer hardware, deep learning has made significant progress in the application of unstructured data, such as images (Cao & Chen, 2025) and text (Li et al., 2024). Specifically, the success of representation learning (Wang & Lian, 2025; Zhang et al., 2025) has gradually replaced the earlier approaches of transforming unstructured data into structured formats. The key to the success of representation learning lies in leveraging a large number of parameters for backpropagation, enabling the model to adapt to data with non-normal distributions. Although models based on backpropagation neural networks (Yang et al., 2019; Banerjee et al., 2023) have achieved significant technical advancements, their application in many sensitive domains, such as medicine (Zhang et al., 2025) and industrial inspection (Rathee et al., 2021), still faces considerable challenges due to the difficulty in understanding the basis of their decision-making. Explainable Artificial Intelligence (XAI) aims to reveal the inner mechanisms of neural network decisions, thereby making these models more reliable for applications in sensitive domains. In recent years, several studies (Li et al., 2025; Jing et al., 2025; Liu et al., 2024; Guan et al., 2024) have focused on injecting explainability into deep learning models and using various visualization techniques to explain the decisions of these "black box" models. While these models have achieved a certain level of interpretability, two pressing issues remain (Huang & Marques, 2023; Huang & Marques, 2024): first, whether the correlations between different attributes are correctly evaluated, and second, whether the model's decision-making pathway truly aligns with human reasoning, even when the model's understanding appears consistent with user expectations.