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

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 .