Events

Zoom Registration: https://umass-amherst.zoom.us/meeting/register/r9F5BXB5QleCFE2zsRKEvg Abstract: Purpose: 1) Explain the scientific foci of ASU Roybal Center for Older Adults Living Alone with Cognitive Decline to delay the onset and progression of Alzheimer’s disease and related dementia (AD/ADRD), and 2) Guide investigators to develop strong research proposals, focusing on the significance of a proposed solution, mechanism-driven and technology-enabled interventions, and health disparity factors.  Rationale: Poor lifestyle behaviors such as physical inactivity, unhealthy diet, and stress contribute to up to 40-50% of AD/ADRD cases; however, few intervention successes have been translated into real-world impacts. Most interventions are not mechanism-driven, precise, accessible, cost-effective, and/or scalable, which could potentially be addressed by technology through integrating Artificial Intelligence, real-time analytics and feedback, enhancing user autonomy and person-centeredness, and personalizing intervention prescription and delivery. In addition, the population of older adults living alone…

Zoom Registration: https://umass-amherst.zoom.us/meeting/register/VWDnTLPlTHGGmEtmUeE_7w#/registration Abstract: Artificial intelligence holds promise to transform care for older adults, yet today’s AI systems routinely underperform for this population due to poor data representation, limited validation, and weak alignment with lived experience. Drawing on a six-month collaboration between the SCAN Foundation, CHAI will be synthesizing evidence from literature review, expert interviews, and multi-stakeholder roundtables to surface why AI fails older adults—and what must change. They will outline practical pathways for building equitable AI, including multimodal data integration, standardized validation, local testing, and patient-centered deployment. The talk concludes with a roadmap for developing trustworthy AI that meaningfully improves outcomes for aging populations. Biography: Lucy Orr-Ewing, Head of Policy & Strategy, Coalition for Health AI (CHAI) Lucy Orr-Ewing leads Policy and Research for CHAI, where she leads policy engagement at both the state…

https://www.youtube.com/watch?v=XST7eqXmFQQ Abstract: Phishing attacks pose a significant threat to users, especially older adults. Existing defenses mainly focus on phishing detection but often cannot explain to lay users why a message is malicious. In this talk, I will discuss how we use Large Language Models (LLMs) to detect SMS phishing while generating evidence-based explanations. The key challenge is that SMS is short, lacking the necessary context for security reasoning. We develop a prototype called SmishX that gathers external contexts to augment the chain-of-thought (CoT) reasoning of LLMs and facilitate the explanation process. I will further discuss our user studies to evaluate the effectiveness and usability of SmishX. Finally, I will discuss the open challenges and opportunities of using AI to help older adults better protect themselves from cybersecurity threats in general. Biography: Gang Wang, PhD, Associate…