Mariia Lesina

Topic: AI data labelling: a pathway or peril to refugee self-reliance?

Objective: Policy analysis or case study on how the increasing use of AI in asylum, migration or citizenship determination procedure can be problematic with regard to statelessness. 

Outcome: Policy Brief

Executive summary: Humanitarian budgets are collapsing at the same moment that AI systems are generating major demand for high-quality human-labelled data. Considering flexibility and low entry requirements of AI data labelling, social enterprises have moved quickly to position refugees at this intersection, framing this digital work as a scalable, dignified pathway to economic self-reliance. Drawing on organisational documents, social enterprise impact reports, and first-hand worker testimonies, the policy brief examines whether data labelling constitutes a meaningful livelihood solution for displaced populations or whether it reproduces new forms of digital precarity within global AI supply chains.
The analysis centres on four interrelated dynamics: the dual identity of social enterprises, which pitch their impact-oriented services to tech clients while selling the same work as empowerment to humanitarian donors; the self-reliance narrative promoted by social enterprises; and the lived realities behind this narrative, including both the positive aspects of giving refugees access to AI work and structural labour harms embedded in this labour. While social enterprises are providing hope and some insulation for refugees entering the labelling market, they cannot overcome structural harms of the market, leaving refugees to bear the cost of that failure. This paper recommends that UNHCR publish binding operational guidelines for social enterprises, drawing on the Fairwork Framework, to ensure that AI-related digital work can support refugee livelihoods without entrenching the very exploitation it claims to address.