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Interpretable AI for Well-Being Using Mobile Health

Mobile health (mHealth) and other highly pervasive, interactive and adaptive digital systems offer an enormous opportunity for enabling theory-based health behavior-change interventions that are replicable, scalable, and sustainable. Such advances could have great impact, and they offer great opportunities for advances in artificial intelligence (AI), psychology, and social science. Unfortunately, the technical challenges of developing AI to support healthier behaviors generally fall into the category of “not suitable for machine learning.” This presentation will summarize recent research with the Fittle+ mHealth systems and predictive models of the daily effects of individual-level mHealth interventions. The models are developed in a computational cognitive architecture and rely on Instance Based Learning Theory and ACT-R learning mechanisms.