EMPATHIA: Multi-Faceted Human-AI
Collaboration for Refugee Integration

Abstract

Current AI approaches to refugee integration optimize narrow objectives such as employment and fail to capture the cultural, emotional, and ethical dimensions critical for long-term success. We introduce EMPATHIA (Enriched Multimodal Pathways for Agentic Thinking in Humanitarian Immigrant Assistance), a multi-agent framework addressing the central Creative AI question: how do we preserve human dignity when machines participate in life-altering decisions? Grounded in Kegan's Constructive Developmental Theory, EMPATHIA decomposes integration into three modules: SEED (Socio-cultural Entry and Embedding Decision) for initial placement, RISE (Rapid Integration and Self-sufficiency Engine) for early independence, and THRIVE (Transcultural Harmony and Resilience through Integrated Values and Engagement) for sustained outcomes. SEED employs a selector–validator architecture with three specialized agents—emotional, cultural, and ethical—that deliberate transparently to produce interpretable recommendations. Experiments on the UN Kakuma dataset (15,026 individuals, 7,960 eligible adults 15+ per ILO/UNHCR standards) and implementation on 6,359 working-age refugees (15+) with 150+ socioeconomic variables achieved 87.4% validation convergence and explainable assessments across five host countries. EMPATHIA's weighted integration of cultural emotional, and ethical factors balances competing value systems while supporting practitioner–AI collaboration. By augmenting rather than replacing human expertise, EMPATHIA provides a generalizable framework for AI-driven allocation tasks where multiple values must be reconciled.

System Architecture

EMPATHIA's Human-AI Collaborative Framework demonstrates how artificial intelligence amplifies rather than replaces human wisdom through three developmental phases: SEED (initial placement honoring individual narratives), RISE (identity construction through meaningful participation), and THRIVE (transcultural harmony enriching both refugee and host communities).

Dataset

The evaluation was conducted on the UN Kakuma dataset from the United Nations High Commissioner for Refugees (UNHCR), comprising detailed profiles of 15,026 individuals from 12 countries of origin, aged 0-95, with diverse educational, skill, and family backgrounds. For access to the dataset, please request it directly here from the UNHCR library.

Citation

@article{barhdadi2025empathia,
  title={EMPATHIA: Multi-Faceted Human-AI Collaboration for Refugee Integration},
  author={Barhdadi, Mohamed Rayan and Tuncel, Mehmet and Serpedin, Erchin and Kurban, Hasan},
  journal={arXiv preprint arXiv:2508.07671},
  year={2025},
  url={https://arxiv.org/abs/2508.07671}
}