Human-AI Co-Interpretation Key for Responsible AI in Critical Fields

Large Language Model (LLM) outputs are increasingly used in fields requiring justified interpretations based on textual evidence and normative standards, such as law, education, and policy analysis. However, a significant failure mode, termed "interpretive misplacement," occurs when model-generated readings are accepted as definitive meanings without an explicit interpretive frame (sources, scope, normative commitments), without preserving alternative interpretations, and without clear provenance. This misplacement risks not only factual errors but also a loss of accountability, as users cannot reliably assess the implications of an output or its underlying basis. Drawing on philosophical hermeneutics, this paper discusses these risks and derives design principles for structuring human-AI co-interpretation. The paper synthesizes existing scholarship on hermeneutics and AI, organizing it into arguments and design-relevant gaps. It posits LLM outputs as candidate readings, reserving true hermeneutic understanding for accountable human interpreters within disciplinary traditions. Human-AI interaction is reframed as an AI-mediated interpretive loop, distinguishing hermeneutic understanding from token-prediction. This foundation leads to design patterns for responsible LLM use in interpretive settings and discusses implications for legal practice, educational assessment, scholarly production, and public discourse, advocating for "digital hermeneutics" as a new literacy.