Operationalizing AI for Good: Spotlight on Deployment and Integration of AI Models in Humanitarian Work

TL;DR

This paper discusses the practical process of deploying, maintaining, and collaborating with humanitarian organizations to operationalize AI models for real-world impact in resource-constrained settings.

Contribution

It provides detailed insights into deployment, collaboration, and maintenance of AI models in humanitarian contexts, addressing a gap in existing AI for Good literature.

Findings

Successful deployment in resource-limited environments

Strategies for ongoing model maintenance and updates

Key lessons for practitioners in humanitarian AI deployment

Abstract

Publications in the AI for Good space have tended to focus on the research and model development that can support high-impact applications. However, very few AI for Good papers discuss the process of deploying and collaborating with the partner organization, and the resulting real-world impact. In this work, we share details about the close collaboration with a humanitarian-to-humanitarian (H2H) organization and how to not only deploy the AI model in a resource-constrained environment, but also how to maintain it for continuous performance updates, and share key takeaways for practitioners.