Definition
Expectation effects refer to the way prior framing or labeling of an AI system biases how people perceive its intelligence, behavior, and trustworthiness, independent of its actual outputs. Telling users a model is a top-tier “flagship” versus a weaker system can change how competent it seems, how people engage with it, and how favorably they rate it, even when the underlying model is identical.