Technofixing the Future in Mining Industry: Ethical Side Effects of Using AI and Big Data to Meet the SDGs

Abstract

Recent advances in artificial intelligence (AI), big data, and non- geostationary satellite (NGSO; LEO/MEO) services promise faster , safer , and “ greener ” mining , but also raise ethical and governance risks . This study interrogates the technofix narrative. Objectives were to map NGSO+AI applications across the mining value chain ; assess technical , operational , environmental , and economic performance; examine governance , data rights , and justice implications ; evaluate capacity and procurement models ( with an East African lens ); and distill actionable guidance . Following a PRISMA-2020 protocol , a mixed-methods review ( database inception –12 Aug 2025) of peer- reviewed and grey literature was undertaken with duplicate screening and appraisal (JBI, RoB 2/ROBINS-I, AACODS; GRADE/ CERQual ). Over 80 empirical studies and initiatives were synthesized ; random-effects meta- analysis was used where outcomes were comparable, alongside realist narrative synthesis . NGSO connectivity reduced latency (LEO: tens of ms; MEO: ~100–200 ms) and high-revisit EO (SAR/ optical ) improved surface-change detection ; operational gains ( uptime , reporting ) were noted but with low – moderate certainty given short follow -up and sponsorship . Governance lagged capability : data ownership and portability were unclear , third-party audit access rare, and community participation uneven ; ethical risks included bias , privacy , and cultural impacts . East African pilots showed technical promise amid institutional gaps. NGSO+AI can advance SDG- aligned mining only when coupled to binding data rights , independent assurance , participatory pathways , open interfaces, and local capacity ; otherwise tools risk performative compliance rather than accountable , just outcomes .