Emerging Directions in Leveraging Machine Intelligence for Explainable and Equity-Focused Simulation Models of Mental Health

Abstract

Simulation models support policymakers, clinicians, and community members in identifying and evaluating interventions to improve population health. While these models are particularly valuable to measure the fairness of interventions, such measurements may require simulating massive populations in order to isolate effects for specific groups (e.g., by race and ethnicity, gender, age). This can create a computational bottleneck, forcing tradeoffs such as simplifying a model (thus potentially losing accuracy) or running fewer simulations (thus accepting wider confidence intervals) in exchange for sufficiently large populations. In addition, policymakers, clinicians, and community members can be involved at the design stage of a simulation model but its complex set of rules often tends to preclude participation at later stages. This discussion considers the use of Machine Intelligence to tackle both challenges, by automatically scaling up simulations and explaining them to stakeholders. This potential is illustrated through the public health challenge of mental health, focusing on agent-based models for suicide prevention.