Predictive analytics in humanitarian action: a preliminary mapping and analysis
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This rapid review research provides the most comprehensive mapping and analysis of predictive analytic initiatives in humanitarian aid to date. It documents 49 projects including a variety of novel applications (see Appendix for details). It provides a typology of predictive analytics in digital humanitarianism and answers a series of key questions about patterns of current use, ethical risks and future directions in the application of predictive analytics by humanitarian actors. The study took 14 days in May 2020. Forty-nine predictive analytics projects were mapped and analysed according to the main phases of the humanitarian cycle, type of predictions made, sector of application, geography of application, and technical approach used. Despite the limitations of rapid response research, some preliminary recommendations are made on the basis of the findings including: i) Governments, humanitarian agencies, funders and private companies should publish more open data in order to further extend the potential for predictive analytics; ii) Humanitarian agencies should apply the precautionary principle in data collection, data safeguarding and responsible data to protect vulnerable populations from harm; iii) To align practice with humanitarian principles and commitments, predictive analytics actors need to include affected populations in all aspects of the design and project cycle; iv) Funding of predictive analysis should be tied to risk assessment, risk mitigation and knowledge sharing on the ethics and downside-risks of predictive analytics; v) Funders should support the emerging ecosystem to develop geographical or thematic specialisms, convene knowledge-sharing events and produce ethical guidelines for practice; vi) Further research is necessary to build on this preliminary mapping and analysis in this crucial and rapidly developing area of humanitarian action vii) Primary research interviews with humanitarian agencies and key informants would make it possible to validate claims and establish the current status and future plans of initiatives; viii) A small number of case studies would improve depth of understanding about approaches being used and proposed pathways to scale; ix) Focus groups or a workshop would surface agency experience of risks and barriers not shared in publicly accessible documents and enable lesson learning.
CitationHernandez, K. and Roberts, T. (2020). Predictive Analytics in Humanitarian Action: a preliminary mapping and analysis. K4D Emerging Issues Report 33. Brighton, UK: Institute of Development Studies.
Is part of seriesK4D Emerging Issues Report;33
Rights holder© DFID - Crown copyright 2020
- K4D