Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Harnessing Artificial Intelligence to Improve Food Assistance: A Scoping Review of Machine Learning Tools

Version 1 : Received: 11 July 2022 / Approved: 14 July 2022 / Online: 14 July 2022 (12:12:01 CEST)

How to cite: Martin, N.M.; Sedoc, J.; Poirier, L.; Rosenblum, A.J.; Reznar, M.M.; Gittelsohn, J.; Barnett, D.J. Harnessing Artificial Intelligence to Improve Food Assistance: A Scoping Review of Machine Learning Tools. Preprints 2022, 2022070221 (doi: 10.20944/preprints202207.0221.v1). Martin, N.M.; Sedoc, J.; Poirier, L.; Rosenblum, A.J.; Reznar, M.M.; Gittelsohn, J.; Barnett, D.J. Harnessing Artificial Intelligence to Improve Food Assistance: A Scoping Review of Machine Learning Tools. Preprints 2022, 2022070221 (doi: 10.20944/preprints202207.0221.v1).

Abstract

Background: Machine learning has revolutionized situational awareness during disaster management by classifying, clustering, and predicting impacted locations and people. Despite its importance, no review has been conducted on machine learning tools for food assistance efforts during emergency or non-emergency situations. The purpose of this scoping review is to address that gap. Methods: Keywords were defined within the concepts of food assistance and machine learning. After the database searches, PRISMA guidelines were followed to perform a partnered, two-round scoping literature review. Text mining and Latent Dirichlet Allocation topic modeling algorithms were used to determine trends. Results: 28 articles met criteria and were included in the analysis. The types of study designs included: model development (42.9%), non-study (i.e., text and opinion) (28.6%), qualitative research (14.3%), case study (10.7%), and meta-analysis (3.6%). There were no quantitative studies. The machine learning tools’ main functions were improving SNAP programs (32.1%), detecting needs and resources (25%), predicting food insecurity (21.4%), and situational awareness of current food insecurity issues (21.4%). None of these studies took place during a disaster or explicitly addressed emergency mitigation, preparedness, or recovery. All of the studies were in early phases of development and implementation. Conclusion: Machine learning tools for improving situational awareness, resource allocation, policymaking, and prediction have the potential to improve food assistance, but there is a lack of implementation and evaluation during all disaster phases. Also needed is more formative work on generating food-related queries and defining variables and features of food security.

Keywords

Artificial intelligence; machine learning; food security; food assistance

Subject

MEDICINE & PHARMACOLOGY, Nutrition

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