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

Migrant disappearances have become a critical humanitarian crisis on global scale. However, the lack of strong, consolidated data across irregular migration routes is a barrier to effective intervention. In this paper, we aim to employ a sociotechnical systems perspective to design an ethics-infused infrastructure. Our primary objective is to convert multimodal digital traces into actionable intelligence, thereby supporting search, identification, and policy accountability. To standardize the analysis of decentralized digital evidence, we define a typology of 32 trace types encompassing ten domains, categorized from geospatial markers to psychosocial signals. This classification is the basis for the modular architecture of five linked cores. We present a novel approach that combines entity resolution and spatiotemporal reconstruction with provenance tracking and explainable AI to support effective humanitarian action. These features enable robust early warning capabilities and provide decision support aligned with OCHA standards. The framework aims to facilitate compliance with FAIR data principles and international human rights standards through embedding cross-layer safeguards including dynamic consent, auditability and interoperability. This research provides a scalable, replicable method to validate digital evidence throughout the migration cycle. It bridges computational innovation and rights-based governance. Future work should focus on operational pilots to assess feasibility, triage efficiency, identification accuracy, and family notification protocols.