The Ghost Workforce of Generative AI: Reclaiming Ethical Visibility in AI Labour

Generative AI systems are widely framed as autonomous technologies, yet they depend on extensive human labour data annotation, content moderation and reinforcement learning—which remains largely invisible within dominant narratives of automation and AI ethics. Drawing on 80 semi-structured interviews across eight countries in Sub-Saharan Africa and the Arab Gulf, this study conceptualises generative AI as a labour-intensive communicative infrastructure and examines how invisibility is systematically produced in AI supply chains. This article introduces the Ethical Visibility Framework (EVF), which identifies three interlocking forms of invisibility: material invisibility, enacted through outsourcing, contractual opacity and wage suppression; epistemic invisibility, whereby workers’ situated knowledge is extracted while their status as legitimate contributors is denied; and narrative invisibility, sustained through automation discourse that erases human labour from accounts of AI performance. These dimensions reinforce one another, enabling economic extraction, expertise appropriation and responsibility deflection. By centring AI workers as epistemic agents rather than peripheral service providers, the study extends scholarship on digital labour, data justice and critical AI ethics, concluding that labour visibility is a foundational ethical requirement for legitimate AI development, not an ancillary concern, with implications for governance frameworks that treat AI ethics as technical optimisation rather than socio-technical justice.