Personalizing AI Interventions in Multiple Health Behavioral Change Settings
Samantha Marks · Michelle Chang · Eura Nofshin · Weiwei Pan · Finale Doshi-Velez
Keywords:
reinforcement learning
multiple health behavioral change
mHealth
personalization
MHBC
mobile health
RL
Abstract
We introduce a novel reinforcement learning (RL) framework for personalizing AI interventions in multiple health behavior change (MHBC) settings. Our key contribution is a simple, interpretable model that captures empirically observed human behaviors. Using this model, we provide insight into how the AI will intervene, including when it has varying degrees of knowledge about the human model.
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