The PAI framework measures parasocial attachment patterns in AI-child interactions, suggesting significant reliability in AI assessments.
The Parasocial Attachment Indicators (PAI) framework is a five-dimension instrument for measuring parasocial attachment patterns in synthetic AI-child conversation transcripts. PAI scores are produced by a cross-lab ensemble of five frontier LLM judges (Anthropic, OpenAI, Mistral, Google, Meta) with a pre-registered Cohen's kappa >= 0.700 reliability gate. This release accompanies the v0.1 preprint and includes the rubric, scoring formulas, reliability methodology, audit-chain specification, and the publicly released judge-bias audit report (n=29 calibration transcripts + n=463 production transcripts, kappa_avg = 0.85 on calibration).
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Chris Gatson (2026) studied this question.
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