# Exploring Local Explanation of Practical Industrial AI Applications: A Systematic Literature Review
## 2 Literature Review Methodology  
This section outlines our methodology for conducting a comprehensive literature review on local explanation techniques and their practical applications in industry. Our discussion of local explanation techniques and their practical applications in industry is built upon a thorough and objective literature study, made possible by a rigorous search strategy, inclusion and exclusion criteria, data extraction and analysis, and quality assessment. The workflow of the survey methodology is shown in Figure 1. We used a clear process that included the following phases to attain this goal:  
1. _Identifying the research question:_ Our research question focused on understanding the different types of local explanation techniques used in industry, their benefits and limitations, and their effectiveness in explaining complex machine learning models. Our review aims to answer the following research questions: * (Q1) What local explanation techniques are used in industrial applications? * (Q2) How widespread are practical industrial applications of local explanation techniques? Figure 1: The workflow of the survey on the local explanation for industrial AI applications. * (Q3) What are the benefits and limitations of local explanation techniques for industrial applications? * (Q4) How to build effective local explanation techniques in practical settings? By answering these questions, we hope to provide a comprehensive overview of the current state of the art in local explanation techniques for practical industrial applications. 2. _Identifying relevant literature_: We conducted a comprehensive search of relevant academic and industry sources, including peer-reviewed journals, conference proceedings, technical reports, and gray literature. The search included studies published from 2020 to Mar 2023. We used a combination of keywords and controlled vocabulary terms related to machine learning, interpretability, local explanations, and industrial applications to identify relevant articles. We specifically examined academic resources such as ACM Digital Library, IEEE Xplore, ScienceDirect, Google Scholar, MDPI, and others. The search terms used include \"local explanation techniques\", \"model interpretability\", \"explainable artificial intelligence\", \"XAI\", \"machine learning\", and \"industrial applications\". We also manually searched relevant journals and conference proceedings to ensure comprehensive coverage of the literature. 3. _Screening and selection of studies_: We used a two-stage screening process to identify articles for inclusion in our review. In the first stage, we screened titles and abstracts to identify potentially relevant articles. In the second stage, we screened the full text of articles to determine their eligibility based on our inclusion and exclusion criteria. We included studies focusing on local explanation techniques and their applications in industrial settings. Studies investigating the effectiveness and limitations of different explanation techniques and their comparative analysis were also considered. We excluded studies focusing on global explanations, theoretical aspects of model interpretability, or applications in non-industrial settings. 4. _Data extraction and quality assessment_: Data from the selected papers were extracted, including study design, sample size, research question, methods, results, and limitations. We extracted data from the selected studies, including the authors, publication year, research question, methodology, datasets used, and main findings. The data were analyzed thematically to identify patterns, trends, and research gaps in the literature. We also performed a qualitative synthesis of the studies, highlighting the benefits and limitations of local explanation techniques and their effectiveness in industrial applications. 5. _Data synthesis and analysis_: We synthesized the data from the selected articles using a thematic analysis approach.