ABSTRACT: As AI systems increasingly make decisions without visible interfaces, the user experience is shaped less by interaction and more by outcome. This shift brings new challenges—especially when adaptive systems begin to misinterpret their purpose or apply the wrong logic in high-stakes environments like finance, public services, or healthcare.

This talk introduces the TAR Framework—Trust, Alignment, Recourse—a governance model developed by me and my team at Phase 5 to address what we’ve identified as AI Identity Drift. We’ll explore how AI can quietly lose alignment with its original role, and how UX and design teams can ensure these systems remain explainable, grounded, and challengeable.

The framework is grounded in UX and CX principles, but it extends into governance, compliance, and institutional trust—offering a practical, experience-centered approach to designing and overseeing AI systems that evolve over time.

Watch the presentation.