Human-AI Co-Interpretation for Responsible AI: A Hermeneutic Perspective

arXiv cs.AISeptember 2, 2026en

arXiv cs.AI

AI Global Wire

arXiv:2609.00334v1 Announce Type: new Abstract: Across law, education, policy analysis, and public moral argumentation, LLM outputs are being used often for work that requires interpretations to be justified with textual evidence and explicit normative standards. Yet a recurrent failure mode -- what I call \textit{interpretive misplacement} -- is that model-generated readings get treated as settled meanings without an explicit interpretive frame (sources, scope constraints, normative commitments), without preserving defensible alternatives, and without provenance that lets readers find the supporting passages. In such settings, the risk is not only factual error but lost accountability: read

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