Knowledge and skills involved in the creation of a model must be sustainable. To enhance cost-effectiveness and contextual learning, capacities should be retrieved from local populations that are familiar with the problem the AI/ML project is trying to optimise for, and working with country teams can lead to simplification and enhanced efficiency. However, lack of data fluency may result in tendencies to incorrectly interpret a system's output, overestimate its predictive capacity or otherwise over-rely on its outputs. Thus, when approaching this decision point, organisations should consider investing in building local technical skills, or in a network builder role required to bridge sectors (e.g. between NGOs and the local tech community). Development of user manuals, including tools to manage staff capacities and staff turnover risks are also highly encouraged.

Please find below a legend of what can be found within the framework:

📚Resources - e.g. reports, articles, and case studies

🛠Tools - e.g. guidelines, frameworks and scorecards

🔗Links - e.g. online platforms, videos, hubs and databases

❌Gap analysis - tools or resources are currently missing

👥 List of stakeholders which should be included in the specific decision point