The process industries are fully embracing digitalization and artificial intelligence (AI). Industry 4.0 has also transformed the production structures in the process industries to increase productivity and profitability; however, this has also led to emerging risks. The rapid growth and transformation have created gaps and challenges in various aspects, for example, information technology (IT) vs. operation technology (OT), human vs. AI, and traditional statistical analysis vs. machine learning. A notable issue is the apparent differences in decision-making between humans and machines, primarily when they work together. Contradictory observations, states, goals, and actions may lead to conflict between these two decision-makers. Such conflicts have triggered numerous catastrophes in recent years. Moreover, conflicts may become even more elusive and confusing under external forces, e.g., cyberattacks. This focuses on exploring human-machine conflict through a systematic literature review on the impact of digitalization on process safety, highlighting the myths and misconceptions of data modeling on process safety analysis, and attempting to clarify associated concepts in the area of human-machine conflict. It illustrates the evolutional process of and measures the conflicts, develops the risk assessment model of conflicts, and explores the condition of conflict convergence, divergence, and resolution. The study demonstrates that conflict is another more profound and implicit phenomenon that raises risks more rapidly and severely in industry. Conflicts are highly associated with faults and failures. Various factors can trigger human-machine conflict, including sensor faults, cyberattacks, human errors, and sabotage. With this study, we attempt to provide the readers with a clear picture of human-machine conflict, alert the industry and academia about the risk of human-machine conflict, and emphasize human-centered design.
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St. John's, NL