Towards synthetic data justice for development: A case study of synthetic datasets on human trafficking

The rise of synthetic data has begun to inspire novel data-driven projects in highly sensitive development contexts. For example, a collaboration between the UN's International Organization for Migration and Microsoft Research has resulted in the release of four “Global Synthetic Datasets” (GSDs) on human trafficking—the 2025 iteration of which contains information on over 222,000 trafficking survivors. These datasets are expected to support the combat against human trafficking by leveraging the privacy promised by synthetic data. While existing scholarship has explored political-economic, ethical, and legal implications of synthetic data, this article presents a situated case study of the GSDs, exploring data justice issues that arise when synthetic data are used in global development projects. Drawing on Linnet Taylor's 2017 data justice framework, this paper asks: how do synthetic data both extend and reconfigure questions of political economy, (in)visibility, disengagement, and non-discrimination in development contexts? Methodologically, the article employs a technography of publicly available materials related to the GSDs, additionally drawing from three key informant interviews. The analysis highlights that while synthetic data-driven development projects enable new knowledge, they continue long-standing critical concerns around the political economy of “data for development,” the visibilization of marginalized communities, and the datafied abstraction from lived experience. Thus, development scholars and practitioners ought to recognize the risk of “ synthetic-washing ,” where faith in the presumed safety and privacy of synthetic data obscures their entanglement with power asymmetries. This underlines the need for further research on frameworks for synthetic data justice , in development contexts and beyond.