# Exploring Local Explanation of Practical Industrial AI Applications: A Systematic Literature Review
## 5 Conclusions and Future Directions  
In conclusion, this literature review has provided insights into the industrial applications of local explanation techniques for ML models. We have discussed the benefits and limitations of these techniques and highlighted the challenges in their practical use. Our findings imply that local explanation strategies can help to improve the interpretability and transparency of black-box models in various industrial settings, including banking, healthcare, and manufacturing. However, limitations and roadblocks remain, such as computational costs and trade-offs between model accuracy and interpretability. Consistent evaluation measures, datasets, and benchmarks are required to address these issues to enable fair and reliable comparisons of diverse methodologies. Furthermore, the human-centered design of local explanations should be considered, as the ultimate goal is to enable human users to understand and trust AI systems. To advance the field, we recommend that future research focuses on developing more effective and efficient local explanation techniques that can be applied to large-scale datasets and complex models. Additionally, ethical considerations must be addressed to ensure the responsible use of these techniques and minimize potential harm to stakeholders. It is crucial to highlight limitations to this literature evaluation, such as the study scope and inherent biases in the selection criteria and methods. Nonetheless, this review contributes significantly to the field and can be a resource for researchers and practitioners interested in XAI and industrial applications. To summarize, local explanation techniques can potentially increase the transparency and interpretability of complex ML and DL models in industrial settings. Further research, however, is required to address the challenges and limitations of these techniques and ensure their responsible use. Conceptualization, T.-T.-H.L. ; methodology, T.-T.-H.L. and A.T.P. ; software, T.-T.-H.L. and Y.E.O. ; validation, T.-T.-H.L. and H.K. ([PERSON]); formal analysis, T.-T.-H.L., Y.E.O. and A.T.P. ; investigation, H.K. ([PERSON]); resources, T.-T.-H.L. and A.T.P. ; data curation, T.-T.-H.L., Y.E.O. and H.K. ([PERSON]); writing--original draft preparation, T.-T.-H.L., Y.E.O., A.T.P. and H.K. ([PERSON]); writing--review and editing, T.-T.-H.L. and H.K. ([PERSON]); visualization, T.-T.-H.L. and A.T.P. ; supervision, T.-T.-H.L. and H.K. ([PERSON]); project administration, H.K. ([PERSON]); funding acquisition, H.K. ([PERSON]). All authors have read and agreed to the published version of the manuscript. **Funding:** This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2023-2020-0-01797) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation), and in part by the Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.2021-0-00903, Development of Physical Channel Vulnerability-based Attacks and its Countermeasures for Reliable On-Device Deep Learning Accelerator Design, 50%). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Not applicable. **Conflicts of Interest:** The authors declare no conflict of interest. **Abbreviations**  
The following abbreviations are used in this manuscript: