How can AI systems balance autonomy and privacy with regards to need to train models on aging populations, particularly when monitoring health and behavior in vulnerable individuals?
In what ways might AI models trained on non-representative (e.g. cross-country) health datasets exacerbate inequalities in aging outcomes across society?
How reliable are current AI models in predicting age-related diseases (e.g., dementia, frailty) given the complex and heterogeneous nature of the aging process? How accurate do you think these models will ever be able to become?
Can AI-based cognitive assistants effectively enhance mental health and reduce cognitive decline in older adults without fostering overreliance or social isolation?
To what extent can AI contribute to quantifying “biological age” as opposed to chronological age, and how might such metrics reshape interventions in geroscience?
How might -- within China and across the world -- we better integrate AI into aging care systems, labor markets, and intergenerational equity in resource allocation?
What are the methodological challenges in using AI to infer causal relationships between lifestyle factors and aging trajectories in observational datasets?
Traditional models in social epidemiology have relied heavily on theory. What is the role of theory moving forward in an age of big data, machine learning, and high performance computing?