As machine learning (ML) and artificial intelligence (AI) technologies are increasingly applied in international development, development practitioners need to engage in the design and implementation of ML/AI-based projects. While data science skills are necessary to build and maintain this technology, so too is the expertise brought by a diverse range of development practitioners. With their deep understanding of root problems, familiarity with local contextual norms, and the ability to foster engagement of those most affected by the technology, development practitioners can and should shape the responsible use of ML/AI in humanitarian assistance and development projects.
Managing Machine Learning Projects in International Development: A Practical Guide aims to support project managers working with a wide variety of stakeholders in the ML/AI project management process. 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.