Using Gerontology Theory to Guide the Development of Artificial Intelligence to Support Aging-in-Place

Abstract

If artificial intelligence (AI) is to support aging-in-place, determining how, when, and why to apply AI is a crucial endeavor. A seminal gerontology meta-theory called Se-lection, Optimization, and Compensation (SOC) model has promise to conceptualize how AI can play a role in aging-in-place. The model posits that successful aging re-quires selecting goals/domains to apply resources, opti-mizing means to best achieve those goals, and compen-sating for losses by attaining new resources or tapping into unused resources for alternative means of pursuing those goals. In this short paper, we describe the SOC model, and draw links to domains in which AI can sup-port aging in place. For example, AI can assist with health-related decision making (selection), cognitive training and reminders (optimization), and domestic task assistance (compensation). Human-centered considera-tions are provided for implementation of AI in the home.