# 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
#### Recommendations  
Section 3.3.1 discusses the challenges of interpreting complex models and the limitations of local explanations in capturing global model behavior, which can hinder the adoption and trust in AI models. To address these challenges and improve the interpretability and transparency of industrial AI applications, we suggest the following remedial measures based on our analysis and knowledge:  
* _Data quality assurance_: Ensuring high-quality datasets is essential to minimize the impact of challenges associated with local explanation techniques. By performing data cleaning, normalization, and preprocessing, we can ensure that the datasets are high quality and minimize the risks of producing unreliable and inaccurate explanations [149].
* _Model validation_: Thorough model validation and testing should be carried out to ensure that the model is accurate and reliable [145; 146]. Our study suggests involving end-users in the interpretability process, prioritizing the evaluation of the model's interpretability according to the context of use, and providing clear explanations of the model's limitations and assumptions to enhance transparency and trust in the model [150].  
* _Appropriate choice of explanation techniques_: The efficacy of local explanation strategies varies according to model type, dataset, and task. Thus, choosing the most appropriate technique for the application is crucial. Researchers can also develop hybrid models that combine black-box and white-box models' strengths to achieve high performance and interpretability [148].
* _Human-in-the-loop_: A human-in-the-loop approach can improve the quality of local explanations and enhance trust in the model. By including human experts in the decision-making process, we can ensure that the local explanations are relevant and accurate for the intended use case [151].  
Different local explanation techniques have strengths and weaknesses in explaining complex ML and DL models in industrial applications. It is, therefore, essential to select the appropriate technique, ensure data quality, validate models, and include human experts in the decision-making process to minimize the impact of the challenges associated with local explanation techniques. To support our arguments, we draw on the literature, which has shown that the effectiveness of local explanation techniques may vary depending on the model type, dataset, and task [152]. Furthermore, recent research has challenged the assumption that relatively simple ML models are easy to interpret, as interpretability depends on the context of use, the users involved, and complexity [153]. Therefore, our study emphasizes the need for a more human-centric approach to XAI, prioritizing user needs over the explainability of the ML model.  
In conclusion, our paper provides a roadmap for using local explanation techniques in industrial AI applications, addressing the challenges associated with their use and enhancing the models' transparency, interpretability, and trust. However, we acknowledge that our framework is not exhaustive and that further research is needed to develop more robust and effective approaches to XAI in industrial settings.