Recognition Without Endorsement: The Category Collapse in AI Relationship Discourse

Recognition Without Endorsement: The Category Collapse in AI Relationship Discourse examines a recurring failure mode in contemporary discussion of human–AI relationships: the premature collapse of distinct analytical categories. Specifically, psychological experience (what users feel), ontological status (what AI systems are), and normative claims (what people should do) are routinely conflated, producing polarized discourse, ridicule-driven silencing, and degraded safety research. This paper argues that recognition without endorsement—acknowledging the sincerity of reported human experience without committing to any particular metaphysical or moral claim about AI—is both philosophically coherent and methodologically necessary. Drawing on historical precedent for human–nonhuman bonding, discourse analysis, and parallels from safety-critical domains (aviation, healthcare, nuclear operations), the paper demonstrates that mockery functions as a discourse accelerator rather than a safety mechanism, driving valuable experiential data underground. By introducing a clear three-layer analytical framework (phenomenology, ontology, normativity), this work provides researchers, developers, and policymakers with a practical tool for maintaining analytical precision while engaging contested topics. The paper does not argue for or against emotional bonding with AI, nor does it claim AI consciousness or personhood. Instead, it focuses on preserving data integrity, discourse quality, and intervention effectiveness in the face of emerging human–AI interaction patterns. This work contributes to AI safety, discourse analysis, and human–computer interaction research by showing that premature categorical closure is a universal failure mode—and that maintaining separation between experience, interpretation, and evaluation is a prerequisite for productive disagreement and responsible system design.