What happens when an AI system forgets what it was built to do?AI Identity Drift explores how adaptive AI systems—trained to learn but not to remember their purpose—can slowly lose alignment with their institutional role.
The result isn’t just a technical error. It’s a UX and governance crisis: users face decisions they can’t challenge, systems drift without detection, and trust erodes silently.
This series proposes a new framework for AI governance—centered on Trust, Alignment, and Recourse (TAR)—and shows why explainability, stability, and contestability must be built into every AI-driven experience.
If your AI can change itself, your oversight must evolve with it.
Stay tuned for more in our newest white paper, "AI Identity Drift: Toward a New Model for AI UX and Governance".
Here's a preview of the chapters:
Chapter 1: Who Am I Speaking With? The Hidden Crisis of AI Identity Drift
Chapter 2: Why AI Drifts — The Causes of AI Identity Failures
Chapter 3: The Governance Crisis — How AI Identity Drift Undermines Compliance and Accountability
Chapter 4: Building Resilience — Preventing AI Identity Drift Through UX, Compliance, and Auditing
Chapter 5: The AI Trust Framework — A Pillar for UX-Centered AI Governance
