Improving humanitarian needs assessments through natural language processing

Abstract

An effective response to humanitarian crises relies on detailed information about the needs of the affected population. Current assessment approaches often require interviewers to convert complex, open-ended responses into simplified quantitative data. More nuanced insights require the use of qualitative methods, but proper transcription and manual coding are hard to conduct rapidly and at scale during a crisis. Natural language processing (NLP), a type of artificial intelligence, may provide potentially important new opportunities to capture qualitative data from voice responses and analyze it for relevant content to better inform more effective and rapid humanitarian assistance operational decisions. This article provides an overview of how NLP can be used to transcribe, translate, and analyze large sets of qualitative responses with a view to improving the quality and effectiveness of humanitarian assistance. We describe the practical and ethical challenges of building on the diffusion of digital data collection platforms and introducing this new technology to the humanitarian context. Finally, we provide an overview of the principles that should be used to anticipate and mitigate risks.