1. SCOPE This paper addresses a core challenge within the World Mining Congress theme of Resourcing Tomorrow: how to deliver mining operations that are faster, smarter, and more responsible while managing increasing technological complexity and risk. The scope focuses on safety leadership in smart mining environments, examining how digital and AI-enabled tools can be integrated with human leadership competencies to improve safety decision-making in high-risk operational contexts. The study covers mining operations in Peru, including open-pit and underground environments, and considers both mine operators and contractors. Its purpose is to define and empirically inform a Human + Digital Safety Leadership Model that supports practical leadership development and responsible digital transformation. The conceptual foundation of this model emerged from field observations and experience in Peruvian mining operations, where increasing digital visibility and technology does not always translate into improved awareness or faster preventive decisions. This kind of situations motivated the present study. 2. STATE-OF-THE-ART Mining is undergoing accelerated digital transformation driven by advanced analytics, automation, and artificial intelligence (AI). The World Economic Forum (2023) and ICMM (2022) highlight that digital innovation is central to achieving safer, more productive, and more sustainable mining operations. Industry analyses such as Deloitte (2024) further identify digitalization and workforce transformation as defining forces shaping the sector’s future. In safety management, digital tools—including mobile inspection systems, dashboards, real-time sensing, and predictive analytics—enable earlier hazard detection and faster response. However, their effectiveness depends on how human leaders interpret and apply the information generated. Raisch and Krakowski (2021) describe the “automation–augmentation paradox,” noting that AI can either replace or enhance human judgment. When poorly integrated, automation may reduce critical thinking and situational awareness. Trust is also a critical factor. Glikson and Woolley (2020) demonstrate that human trust in AI significantly influences its appropriate use. In high-risk industries, insufficient or excessive reliance on automated systems can both undermine safety outcomes. From a safety science perspective, Hollnagel’s Safety-II framework (2014–2018) emphasizes understanding how work succeeds under variability rather than focusing solely on failure. Le Coze (2022) further argues that digitalization in high-risk sectors introduces new organizational risks, including cognitive overload and shifting boundaries between human and machine decision authority. Leadership and change management literature reinforces that technology adoption alone does not ensure improved performance. Kotter (1996) and Drucker (1954) highlight that sustainable transformation requires leadership clarity, structured change processes, and long-term strategic 31
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