Humanitarian data infrastructures for missing migrants: A multimodal and ethics-integrated framework

Area of Science:

• Humanitarian Action
• Data Science
• Sociotechnical Systems

Background:

• Migrant disappearances present a critical global humanitarian crisis.
• A lack of consolidated data across irregular migration routes hinders effective intervention.
• Sociotechnical systems offer a framework for addressing complex humanitarian challenges.

Purpose of the Study:

• To design an ethics-infused infrastructure for converting multimodal digital traces into actionable intelligence.
• To support search, identification, and policy accountability in migrant crises.
• To standardize the analysis of decentralized digital evidence.

Main Methods:

• Defined a typology of 32 trace types across ten domains for evidence standardization.
• Developed a modular architecture with five linked cores.
• Combined entity resolution, spatiotemporal reconstruction, provenance tracking, and explainable AI.

Main Results:

• Created a novel approach for validating digital evidence throughout the migration cycle.
• Enabled robust early warning capabilities and decision support aligned with OCHA standards.
• Facilitated compliance with FAIR data principles and international human rights standards.

Conclusions:

• The research provides a scalable and replicable method for digital evidence validation in migration contexts.
• The framework bridges computational innovation with rights-based governance.
• Future work includes operational pilots to assess feasibility and effectiveness.