Sustainability over time is a key feature in the lifecycle of an AI/ML product. To foster it, ongoing costs and maintenance must be considered and accounted for. Local capacities for the maintenance of an AI /ML tool must be identified, along with a sustainable system encouraging continuous training and updating. If maintenance, running, or other types of costs are incurred within the process, organisations must identify ways to cover these. Appropriate incentives to maintain community participation over time must also be identified. Staff turnover due to new and evolving staff capacities must be accounted for especially in low-resource settings where procurement processes may largely differ and adequate funding mechanisms may be scarce.

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