Responsible and Trustworthy Artificial Intelligence in Mining, Oil and Gas, and Manufacturing: A Systematic Review

Abstract The integration of artificial intelligence (AI) into mining, oil and gas, and manufacturing industries has created opportunities to improve productivity, efficiency, safety, and reliability. However, the use of AI also raises concerns related to trust, transparency, and ethical considerations. This study presents a systematic literature review of 87 peer-reviewed studies published between 2010 and 2025, examining how the integration of AI affects trust, transparency, and ethical considerations across mining, oil and gas, and manufacturing industries. Following PRISMA 2020 guidelines, relevant publications were identified, screened, and classified based on industry, research focus, methodological approach, applications, and maturity of deployment.The analysis reveals clear differences between industries. Manufacturing demonstrates the highest level of maturity, with strong emphasis on trust, human–robot collaborative system design, and operational transparency, alongside increasing adoption of explainable AI (XAI) methods. Oil and gas shows notable use of XAI driven by safety assurance and risk management requirements, but limited adoption of AI-specific governance and ethical evaluation. Mining remains at an earlier stage of AI adoption, focusing primarily on performance reliability and safety-critical applications. Overall, the review identifies persistent gaps, including reliance on post-hoc explainability, limited validation of explanations, and underdeveloped regulatory alignment, highlighting the need for integrated approaches that link trust, transparency, ethics, and governance in industrial AI systems.