The application of machine learning (ML) and artificial intelligence (AI) in high-stakes domains, such as healthcare, presents both opportunities and risks. One significant risk is the epistemic uncertainty of ML/AI developers, who often lack sufficient contextual knowledge about the complex problems they aim to address and the socio-technical environments in which their interventions will be implemented. Conversely, individuals from civil society who are most affected by these issues and are most vulnerable to the harms that AI systems can cause possess deep, qualitative contextual knowledge that is often overlooked and difficult to incorporate into product development workflows. In this talk, Donald will introduce the problem understanding gap between civil society and AI product developers, which can lead to harmful outcomes. He will introduce community-based system dynamics (CBSD) as a way to bridge this gap and provide structural causal knowledge that can inform product development. CBSD involves working closely with communities to understand the dynamics of the problem being addressed, and leveraging this understanding to develop effective and contextually-appropriate solutions.