# Exploring Local Explanation of Practical Industrial AI Applications: A Systematic Literature Review
## 4 Discussion of Gaps and Limitations in the Current Literature
### Discussion of Survey Results and Gaps  
Section 3 presents our survey results, allowing us to answer the four main research questions outlined in Section 2. Analyzing the data collected gave us insights into using local explanation techniques in industrial applications and their distribution, benefits, limitations, and effectiveness. Q1: What local explanation techniques are used in industrial applications? SHAP, LIME, and Grad-CAM variations are among the most extensively employed local explanation approaches in industrial applications, according to the survey data presented in Section 3.1.1. SHAP assigns an importance score to each feature based on the Shapley value from cooperative game theory. LIME is a model-agnostic explanation method that learns an interpretable model locally around the prediction to explain the predictions of any black-box model. Grad-CAM variations in deep neural networks use gradient information to depict and explain the features contributing to a certain prediction. In numerous industrial areas, these strategies have proven beneficial in delivering local explanations and boosting transparency and trust in AI models. Q2: How widespread are practical industrial applications of local explanation techniques? The practical industrial applications of local explanation techniques have gained significant traction in recent years. According to the survey results in Section 3.1.2, local explanation techniques are widely used in various fields, including healthcare, energy and building management, and industrial engineering. Among these fields, healthcare is one of the most prominent sectors in adopting local explanation techniques. These techniques are used extensively for clinical decision making, disease prediction, and patient monitoring. In healthcare, local explanation techniques play a vital role in helping medical professionals to understand the reasoning behind the predictions of medical diagnosis models, improving the accuracy of diagnosis, and building trust in the models. Energy and building management is another sector that has embraced local explanation techniques to optimize energy consumption and improve building performance. These techniques are used to interpret the predictions of energy consumption models, identify the factors affecting energy consumption, and recommend energy-efficient practices. Similarly, industrial engineering uses local explanation techniques for predictive maintenance and quality control. These techniques can help to identify potential machine failures, predict maintenance needs, and improve product quality. The survey results demonstrate that local explanation techniques are increasingly prevalent in various industrial sectors. Healthcare, energy and building management, and industrial engineering are among the leading fields in adopting these techniques. Q3: What are the benefits and limitations of local explanation techniques for industrial applications? The ability to explain specific predictions of complex models, identify the essential components that lead to the prediction, find biases and errors, and establish trust in the models are all advantages of local explanation techniques. Local explanations are also useful in increasing AI systems' transparency, accountability, and fairness. On the other hand, as outlined in Section 3.3.1, one of the major limitations of local explanation techniques is the lack of standardization in evaluating and comparing them. Although numerous studies have proposed various techniques, there is currently no agreed-upon standard for evaluating their performance, which makes it difficult to compare different methods. This limitation poses a challenge in determining the most effective technique for specific applications. To address this issue, there is a need for standard datasets, metrics, and benchmarks, to enable a fair and accurate comparison of different techniques in different applications [14].