Abstract
Research on AI consciousness has largely focused on whether AI systems are conscious and how humans attribute consciousness to them. Yet large language models (LLMs) increasingly function as consciousness attributors, generating judgments about whether and to what degree other entities are conscious. We introduce model-generated consciousness attribution as an object of empirical operationalization and diagnosis, defining an attribution rule as the recurring relationship between features of a target and an evaluator’s ratings, without implying subjective belief, intention, or experience. An illustrative probe compared the attribution patterns of nine contemporary models with a human reference. Nearly all model runs occupied the same region of the human-derived measurement space, characterized by comparatively strong, positive weighting of metacognitive self-reflection. The models also produced broadly similar rankings of fictional AI characters from movies, while differing in their overall rating levels. We propose a diagnostic agenda organized around three questions: how model attribution is oriented relative to human references, how attribution rules vary across models, and how observed patterns depend on the cue sets, targets, and task formats through which they are measured. As LLM-generated judgments circulate through public, professional, and academic settings, diagnosing these attribution rules can help characterize how AI systems participate in shaping interpretations of AI consciousness. This remains distinct from the ontological question of whether the systems themselves are conscious.