Why are people miscalibrated on AI?

This paper develops a framework for understanding why people form miscalibrated views about artificial intelligence. We argue that miscalibration is not primarily an information deficit problem but an optimization target problem: individuals whose goal is identity preservation rather than accuracy deploy their cognitive capacity selectively, producing motivated reasoning that resists correction.

We formalize this as a multiplicative model — View = Optimization Target × (Information Quality × Processing Quality) — and identify failures along each component, distinguishing between external factors that degrade information and processing independent of the individual and target-driven distortions that systematically bias both from within. We describe a critical property of identity-protective cognition, the self-concealing property, by which the bias is phenomenologically indistinguishable from the epistemic rigor it displaces, making standard interventions of "more awareness" insufficient.

We further argue that miscalibration is compounded by misspecification of the object of the view itself — misattributing outcomes to AI rather than the human system, and treating "AI" as a single entity rather than a bundle of capabilities entering distinct domains. We then outline interventions organized around what organizations can change about the environment and what individuals can do given the limits of self-correction.