Work

Datafied localization: Reproducing unequal power hierarchies in humanitarianism

Year: 2025

Type: article

Abstract: The integration of artificial intelligence, machine learning, and algorithms into development aid and relief efforts has brought datafication processes to the forefront of contemporary humanitarianism. This article contributes to critical studies of (big) data in humanitarianism by introducing insights from the humanitarian literature on distance and proximity. It highlights three ways in which the mobilization of big data to convey “local” perceptions of needs—what we call datafied localization —can perpetuate epistemic injustices and paternalistic logics in humanitarian practice. First, we argue that big data represents a recontextualization rather than a decontextualization of reality, underscoring that data only becomes knowledge through contextualized interpretation. Second, we propose that the question of defining the local becomes even more pertinent in a datafied version of reality as representation takes place at a distance. Third, we argue that the introduction of big data into localization efforts risks reducing local communities to mere data suppliers, thereby reproducing the power imbalances that localization was meant to counter.

Source: Big Data & Society

DOI: 10.1177/20539517241304693

Authors: Maria-Louise Clausen, Adam Moe Fejerskov, Sarah Seddig

Institution: Danish Institute for International Studies

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

SDGs: Reduced inequalities, Peace, justice, and strong institutions

Open Access status: gold

Estimated APC paid: $2,000