A Research Through Design Study on AI Explanations for Collaborative Everyday Tasks for Older Adults Aging in Place

Designing explainable and personalized AI systems to provide support to older adults aging in place requires an understanding of their motivations and expectations for the explanations. This poster presents our ongoing work in exploring explanation preferences within AI systems for older adults aging in place with their carepartners. We do so by leveraging the speculative and iterative benefits of the Research through Design (RtD) approach in HCI, and explore variations in explanation requirements for different users by understanding their needs, goals and motivations for the different sources of information within the home. We illustrate an example for employing a Research through Design inquiry for the design of AI applications, adopting speculative methods to probe into future possibilities of Explainable AI (XAI) using a human-centered design framework. Through a Speed Dating study and a Co-Design activity, we investigate different explanation types and scenarios and argue for a shift in the algorithmic focus of Explainable AI research toward user-centered requirements, positioning explanation as a collaborative process between AI systems and users.