A Comprehensive Review on AI-Powered Compliance Navigator for Mineral Resource Governance

Abstract

The mining industry operates under an intricate network of Acts, Rules, and Regulations. Compliance with these laws is a time-consuming and error-prone process, often requiring domain expertise. Artificial Intelligence (AI) and Natural Language Processing (NLP) have emerged as potential solutions to automate legal compliance. This paper presents a comprehensive review of AI-powered compliance systems, focusing on Legal NLP, Retrieval-Augmented Generation (RAG), and Process Mining techniques applicable to mineral resource governance. Drawing insights from twenty research papers across IEEE, Springer, Elsevier, and MDPI, this review highlights advancements in intelligent chatbots, legal data mining, explainable AI, and automated conformance verification. The paper also identifies existing research gaps and provides a roadmap for developing an AI-Powered Compliance Navigator tailored to the mining domain.

Introduction

The paper reviews how AI-driven technologies—especially Natural Language Processing (NLP) and Process Mining—are transforming mining governance by automating regulatory compliance and interpretation tasks that were traditionally manual and error-prone.

The literature review covers major contributions such as domain-specific chatbots (e.g., MineBot), Legal NLP models (LegalBERT), and Retrieval-Augmented Generation (RAG) systems that enhance factual reliability in regulated sectors. Foundational works on Process Mining and Conformance Checking establish methods to verify compliance using operational data. Additionally, Explainable AI (XAI) and certification frameworks ensure fairness, transparency, and trustworthiness in AI-based governance.

The methodology involves analyzing twenty research papers from reputed journals and classifying them into key themes—chatbots, Legal NLP, RAG, Process Mining, and XAI—to synthesize insights into a unified framework.

The discussion emphasizes that while individual technologies show promise, their integration remains a major challenge due to limited adaptability and scalability. The proposed AI-Powered Compliance Navigator aims to overcome this by merging Legal NLP, RAG, and Process Mining into a cohesive, transparent system tailored for the mining industry’s compliance management.

Conclusion

This review demonstrates that while the field of AI-driven compliance is rapidly evolving, existing systems lack domain generalization and semantic explainability. There is an urgent need for integrated frameworks capable of understanding legal text, retrieving relevant regulations, and validating compliance through process conformance. The proposed AI-Powered Compliance Navigator bridges this gap by unifying NLP, RAG, and Process Mining into a single, explainable system for mineral resource governance. Future work will focus on real-time updating of legal datasets, multilingual adaptation, and improved model interpretability.

References

[1] MineBot: Intelligent Mining Query Assistant, 2023. [2] S. Sharma et al., \'Artificially Intelligent Health Chatbot Using Deep Learning,\' 2022. [3] E. Quevedo Caballero et al., \'Legal NLP from 2015–2022: A Mapping Study,\' IEEE Access, 2023. [4] W. M. P. van der Aalst, \'Workflow Mining: Discovering Process Models from Event Logs,\' IEEE TKDE, 2011. [5] A. Burattin et al., \'Conformance Checking: A State-of-the-Art Survey,\' IEEE SMC, 2020. [6] L. Hillebrand et al., \'Advancing Risk and Quality Assurance: A RAG Chatbot for Improved Regulatory Compliance,\' IEEE BigData, 2024. [7] M. Gupta et al., \'Certifying Generative AI for Regulated Domains,\' IEEE Computer Society, 2023. [8] A. Deroy and N. Bailung, \'Artificial Intelligence in Legal Data Mining,\' 2024. [9] M. Becker and R. Buchkremer, \'A Practical Process Mining Approach for Compliance Management,\' 2019. [10] J. Kim and M. Min, \'QA-RAG for Pharmaceutical Regulatory Compliance,\' 2024. [11] R. Yang et al., \'Retrieval-Augmented Generation: Engineering and Evaluation in Practice,\' 2024. [12] A. Pery et al., \'Trustworthy Artificial Intelligence and Process Mining,\' IEEE Computer, 2021. [13] LegalBERT: Domain-Adapted Transformer Models for Legal Tasks, 2022. [14] Explainable AI in Regulatory Contexts: Challenges and Frameworks, 2023.

Copyright

Copyright © 2025 Sohan Patil, Tejas Gundal, Gaurav Jadhav, Ruturaj Kate. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.