## 2 Related Work
### XAI Technologies  
Modern implementations of the CPS and digital twin are moving away from the traditional consultative approach to a machine learning approach that results from learning process data. It is difficult to adapt to the environment in response to the rapidly changing market demand in the existing method of analysis by humans [18]. However, AI also has chronic problems. Since it is not easy to understand how AI works, the black box characteristics of AI lower its reliability and cause many difficulties in applying it to actual processes. Many studies are being conducted to solve this problem. XAI is a field of research that aims to make the results of AI systems easier for humans to understand [22]. The term was first coined by [PERSON] in 2004 to describe the ability of a system to describe the behavior of AI-based entities in simulation games. Although the term was only coined relatively recently, the problem of 'explainability' has existed since the mid-1970s, when explanations of expert systems were studied. However, since then, AI and ML have developed rapidly, and as AI research places more importance on predictive power than decision-making processes, interest in explanatory power has declined. Recently, it has been receiving renewed attention from academia and practitioners, because as AL/ML penetrates the industry, the influence of AL/ML on decision-making is becoming more important. Accordingly, there is a demand for AI technology that can be explained ethically, socially, and legally. Figure 1: AI cooperation framework. The necessity of XAI research can be explained in four ways, as shown in Figure 2.  
* Justify: Over the past few years, there has been debate about whether AI/ML-based systems are discriminatory or biased. This indicates a growing need for explanations to ensure that AI-based decisions are not made incorrectly. This refers to the need to provide a reason or justification for a particular outcome rather than an explanation of the inner workings of the decision-making process [23]. * Control: The better the understanding of the behavior of a system, the greater the visibility into its vulnerabilities and flaws, and the faster that errors can be fixed, providing greater control [22]. * Improve: A model that can be explained and understood can be more easily improved. Knowing why a particular output was generated can make the system more sophisticated. * Discover: Explanations are needed to learn new facts and gather information. Considering that AI can be superior to humans, if AI can explain the strategies or knowledge it has learned to humans, AI can also inform humans of new laws of natural science that humans are not aware of. Techniques that explain XAI can be divided into two broad categories, feature-based technology and example-based technology. Feature-based technology describes how input features affect the model's output, and example-based technology describes how examples affect the model's output. Representatively, feature-based technologies include Local Interpretable Model-agnostic Explanations (LIME) and SHAP, and example-based technologies include Contrastive Explanation Method (CEM) and Kernel SHAP [24]. The recent success of AI stems in large part from advances in ML algorithms that include SVM, random forest, reinforcement learning, and Deep Learning (DL) models. In particular, research on DL has been actively conducted and has been successfully used in various fields, such as medicine, ophthalmology, autonomous robotics, image processing, and voice and audio processing. However, there is the problem that these AI systems cannot explain their internal mechanisms. The performance (predictability) and explainability of ML are an intrinsic conflict. For example, the best performing DL is the most difficult to explain, and the easiest to explain the decision tree is the least accurate.