Why Most AI Visualizations Fail at the Interface Level(AI UI Design Rejection Specification v1.0 Interface-Level Refusal Rules for Human-Compatible AI Systems)

→ → → CMEM Framework Collection Link This paper examines why a large proportion of AI-generated visualizations fail in practical use, despite being produced from correct data or models. The central claim is that these failures are not primarily due to insufficient information, model capacity, or aesthetics, but arise at the interface level—where representations meet human perceptual and regulatory limits. Using the CMEM framework (Decoding, Echo, Debugging), the paper identifies a recurring imbalance in AI visualization pipelines: representational amplification (Echo) escalates rapidly, while interface-level regulation (Debugging) remains weak or implicit. As a result, many visual artifacts appear impressive at first glance but collapse under sustained inspection, a phenomenon formalized here as second-glance collapse. The paper introduces a minimal diagnostic and a set of interface-level constraints that treat interpretability as a constrained reachability problem rather than a matter of visual style or persuasion. It argues that improving upstream models alone does not resolve these failures, and that stable visualization requires explicit regulation at the perceptual boundary. This work does not propose a new visualization aesthetic or optimization strategy. Instead, it offers a structural diagnostic framework for understanding when and why visual representations cease to function as reliable interfaces between machines and human observers.