Trust Formation in Healthcare AI: An Exploration of Older Adults’ Perspectives

Abstract

As artificial intelligence increasingly shapes healthcare systems, understanding how older adults—who interact with healthcare services more often and face particular difficulties—develop trust in these technologies becomes crucial. While the AIES community has previously examined AI’s social implications across dimensions like gender and race, age remains an understudied axis of analysis. Through a participatory workshop with older adults in Germany, this paper investigates two central questions: (1) How do older adults perceive and experience trust in AI-driven healthcare technologies? (2) What are the key factors that shape trust in AI healthcare technologies among older adults? Our findings reveal that while older people trust certain abilities of AI systems, like medical image analysis, there is a strong emphasis on the necessity of human supervision to trust in these systems. Key trust factors elicited by our study are transparency about training data demographics and algorithmic decision-making processes. More importantly, a gradual exposure to AI systems in non-critical settings, prior positive experience with technology, and cultural context—particularly trust in locally developed systems with clear accountability measures and robust regulatory oversight are key elements in trust formation among older adults. This study offers contextualized insights to guide the equitable, community-driven design, deployment, and governance of AI healthcare technologies, aiming to better serve older populations. By centering inclusivity in technology development and advancing trustworthy AI systems, this work contributes to ethical, effective healthcare solutions tailored to the needs of aging communities.

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