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As AI systems become more sophisticated and autonomous, understanding how they interact with humans in complex, real-world scenarios becomes critical for safety. Traditional testing methods often miss edge cases that emerge from the messy reality of human behavior, environmental pressures, and system interactions. The Gap in AI Safety Testing Current AI safety approaches tend to focus on: Technical robustness (adversarial inputs, model failures) Alignment problems (reward hacking, goal specification) Controlled environments (laboratory testing, synthetic datasets) While these are essential, they often miss a crucial dimension: how AI systems behave when embedded in complex human systems under real-world constraints.

Experience Notation emerged from a simple problem: how do you model complex human behavior in a way that both people and machines can understand? The Challenge Traditional user journey mapping tools fall short when modeling the nuanced decisions, environmental factors, and behavioral patterns that shape real human experience. Flowcharts capture linear processes, but human behavior is rarely linear. Personas describe static characteristics, but people adapt and change. We needed something that could: