Developer Documents How AI Explanations Drift From Structured Project Models

A software developer has published findings from a personal project examining how AI-generated explanations diverge from the underlying relational models they are meant to describe. The study observed that when a carefully structured framework involving reciprocal review and asymmetrical human authority was translated into general public language, the AI repeatedly defaulted to simpler one-way authority descriptions. The author distinguishes between observed patterns within this specific project and broader claims about AI systems, explicitly noting the findings lack external validation. A key practical conclusion is that project-specific structure is not automatically preserved during abstraction, and public-facing summaries must be treated as derived artifacts requiring fidelity checks. The developer recommends that corrections target the specific faulty layer — explanation, interpretation, or model — rather than silently overwriting accepted project state.