Managing Machine Learning Projects in International Development: A Practical Guide

This practical guide has been designed for development practitioners who may not be trained technologists but are involved with or responsible for implementing projects that might have a technical machine learning/artificial intelligence component. The guide breaks down the process of developing a ML/AI model into four phases: evaluating feasibility of the use of ML/AI in a project context, designing and building a ML/AI model, implementing the model in practice, and post-implementation considerations. It highlights critical decisions that a project manager might have to make, and provides decision guides and tips along the way. Throughout the ML/AI project lifecycle, the guide also highlights four themes that are central to the responsible use of ML/AI in development: responsible, equitable and inclusive design; strategic partnerships and human capital; adaptive management for ML projects; and enabling environments for ML/AI. Intended to be a resource that can be consulted in a modular and iterative manner, we hope this guide strengthens productive collaboration between international development and data science practitioners to support the responsible design and use of ML/AI in global development.