Abstract
With 82.4 million forcibly displaced people, we need new approaches to the global refugee crisis. The Hive, the innovation lab at USA for UNHCR, uses data, machine learning (ML), and other emerging technologies to improve lives for refugees in coordination and collaboration with UNHCR (United Nations High Commissioner for Refugees), known as the UN Refugee Agency. We outline five challenges in successfully leveraging data and emerging technologies in the humanitarian space that tend to be overlooked and share the Hiveâs approach and evolution to tackling these challenges. From assembling the right team and finding the right partners to inclusive and impactful data innovation, the Hive has worked to apply industry techniques to the nonprofit sector since 2015. We hope that our insights can help guide data innovation efforts at other organizations in the humanitarian space.
Highlights
The sheer scale of humanitarian crises around the world is daunting
Current trends suggest that the number of those displaced by violent conflicts, natural disasters, and economic crises will continue to increase with the rise of protracted refugee situations (USA for UNHCR, 2020)
UNHCR has had a budget shortfall of on average $3.5 billion since 2016 (UNHCR, the UN Refugee Agency, 2021e), and despite increased exposure of the plight of those displaced, the Refugee Investment Network has found that only 1% of available grantbased philanthropy has been allocated to the refugee space (Kluge et al, 2018)
Summary
The sheer scale of humanitarian crises around the world is daunting. According to the UNHCR (United Nations High Commissioner for Refugees), known as the UN Refugee. The second stage involves the journey heading to a known or unknown destination, where individuals and families may be displaced internally or seeking refuge in neighboring countries. Those who were able to flee may reside in refugee camps, informal settlements, or urban areas These refugees are often in protracted situations, where mass displacement affects a country for more than 5 years, and spend decades living in refugee camps. Within the context of refugees, we’ve seen many practical applications of these technologies across all stages of the refugee journey, from fleeing violent situations to resettlement. We’ve seen innovative solutions for translation and service delivery that leverage machine learning to connect refugees with relevant, tailored resources and services, while providing humanitarian organizations with technology platforms to scale their service offerings. “data science doesn’t have to be sexy to be impactful” (Lewis, 2020)
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