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Short abstract:

The paper explores how refugees in Uganda experience vulnerability assessment tools and offers insights into the broader implications of automated aid prioritization.

Long abstract:

In light of unprecedented global displacement and funding cuts, UN agencies have begun moving away from blanket assistance to prioritized and targeted approaches to aid. The World Food Programme (WFP) and its partners jointly undertook an individual profiling exercise across Uganda’s 13 refugee settlements between 2021 and 2023. It involved the use of several vulnerability assessment tools, including an index-based ranking to identify and provide targeted assistance to the most in need refugees. This led to the automatic categorizing of refugees into three groups (most vulnerable, moderately vulnerable, and least vulnerable) and the removal of the least vulnerable from food assistance.

While the literature on vulnerability in the migration context has increased over the years (Peroni & Timmer, 2013; Sandvik, 2021; Purkey, 2022), the effects of the automation of vulnerability assessments tools on refugees in camps and settlements remain unexplored. Based on field research in the Nakivale and Oruchinga settlements, my paper asks how data-driven vulnerability assessment tools are (re)shaping refugee protection and governance. This research integrates perspectives from science and technology studies (STS), critical data studies and refugee studies, areas of study that have not had much overlap. Using STS concepts such as co-production, my paper explores how refugees in Uganda experience vulnerability assessment tools and offers insights into the broader implications of automated aid prioritization.