Artificial Intelligence Enabled Qualitative Engagement with Conflict Populations
By Kobo, Inc.
In the current humanitarian response system, needs assessment approaches often require interviewers to convert complex responses into simplified quantitative data. More nuanced insights require the use of robust qualitative methods, but severe language barriers and lack of resources make proper transcription almost impossible in conflict and disaster settings. As a result, humanitarian operational decisions frequently fail to consider crucial information about the specific needs and views of affected people—especially the most vulnerable.
Kobo, Inc. will develop a natural language processing (NLP) toolkit for systematic recording, transcribing, and translating of interviews between humanitarian organizations and conflict-affected persons. This toolkit helps overcome language barriers to improve humanitarian response, planning, and accountability, by capturing and understanding community feedback in a more meaningful way. The validity, accuracy, and timeliness of the toolkit will be assessed against professional transcription and translation.