Ontological Risk of Operationalizing Beauty in AI: Design Boundary Constraints for Irreducible Human Values

As AI systems increasingly mediate evaluative judgment through ranking, recommendation, generation, and curation, they do not merely influence user choices but participate in shaping the background conditions under which value is interpreted, renewed, and articu- lated. This paper examines a form of risk insufficiently addressed by existing AI ethics frameworks: the risk that provisional formalizations of evaluative values, once embedded at scale, acquire de facto normative authority and narrow the interpretive space necessary for human meaning-making. I introduce ontological risk as a distinct category of concern, referring not to meta- physical claims but to transformations in the procedural conditions of intelligibility—those conditions under which experiences, artifacts, and practices can appear as meaningful or significant at all. The category is situated relative to adjacent literatures on value alignment, value capture, technological mediation, hermeneutical injustice, and algorithmic homog- enization. Using beauty as a paradigmatic but non-privileged case, the paper identifies structural features that make certain values resistant to final operationalization. I propose four demarcation criteria (D1–D4) for identifying values vulnerable to ontological risk, outline pre-ontological indicators for early detection together with candidate measurement strategies, and advance design boundary principles—including constitutional constraints and modified reinforcement learning from human feedback (RLHF)—aimed at preserving interpretive openness while enabling legitimate AI assistance. The argument does not claim that AI systems must be excluded from evaluative domains. Rather, it advances a precautionary design framework that treats interpretive openness as a minimal procedural condition for sustained human agency and cultural renewal in AI-mediated environments.