Too often, AI risk conversations center around data leakage or model bias- while ignoring the real-world consequences for those most exposed: seniors, disabled users, and non-technical individuals. This talk examines the human cost of AI design that assumes high literacy, digital fluency, or perfect attention. With case studies from scam victims and AI-driven deception, we’ll spotlight failure points in design and deployment- and offer design principles for systems that adapt to human vulnerability instead of punishing it.
Learning Objectives:

Examine how current AI design patterns fail high-risk user populations

Learn principles of inclusive, safety-first AI UX

Build awareness of ethical gaps in real-world AI deployment