# Exploring Local Explanation of Practical Industrial AI Applications: A Systematic Literature Review
## 3 Survey Results on Local Explanation in Industrial AI Applications
### Current Challenges and Recommendations of Local Explanation for Industrial Applications
#### 3.3.1 Challenges  
Local explanations are essential to industrial AI applications but face two significant challenges. The first challenge is that local explanations require computational resources and only provide a narrow view of the model's behavior. The second issue is the trade-off between interoperability and performance accuracy, as a human-centric strategy is still required.  
Local explanation techniques aim to provide insight into the decision-making processes of complex ML models by highlighting the contribution of individual features to the final prediction for a specific input instance. Some of the popular local explanation techniques used in industrial applications include LIME [138], SHAP [139], and Anchors [140]. These techniques have been applied to a wide range of applications such as credit risk assessment [73], image classification [141], and text classification [142]. These techniques provide transparency, increase trustworthiness, and can improve model performance. However, the effectiveness of these techniques may vary depending on the type of model, dataset, and task [138; 139]. Several studies have demonstrated the effectiveness of local explanation techniques in industrial applications. For example, [PERSON] et al. [141] used LIME to explain the predictions of a deep neural network for image classification and showed that the technique improved the model's performance and helped to detect biases in the dataset. [PERSON] et al. [73] used SHAP to interpret the predictions of a credit risk assessment model and found that the technique increased the transparency and trustworthiness of the model. Different local explanation techniques have strengths and weaknesses in explaining complex ML and DL models in industrial applications. Several studies have compared the performance of different techniques, such as LIME, SHAP, and Anchors, on various tasks and datasets [73; 139; 140]. These studies have shown that the effectiveness of these techniques may depend on the type of model, dataset, and task and that no single technique is universally superior.  
XAI has been proposed as a way to address the lack of transparency and limitations of AI's usage in critical fields [143; 14]. The goal of XAI, according to [144], is to provide techniques that let end users comprehend, believe in, and control the rising number of AI systems. For typical learning models with high-dimensional datasets, interpretability and performance accuracy frequently trade off. Models with high architectural complexity, such as DL and random forests, often provide better performance accuracy, but are called \"black-box models\" because they lack transparency and explainability. In contrast, white-box or glass-box models, such as linear, graphical, and decision trees, provide transparent and understandable results. Still, their performance may be subpar [145]. However, recent research by [146] challenges the assumption that relatively simple ML models are easy to interpret, as interpretability depends on the context of use, users involved, and complexity. Therefore, [PERSON] et al. [147] argue for a more human-centric approach to XAI, which prioritizes user needs over the explainability of the ML model. As a result, there may be situations where a high-performance model is preferred over an explainable one, or vice versa [148].