发表机构
School of Engineering and Applied Sciences, Harvard University（哈佛大学工程与应用科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文从解释学视角指出AI解释错位的风险，提出人机协同解释的设计原则，将LLM输出视为候选解读，为法律、教育等领域提供负责任AI的应用方案。
AI 中文摘要
在法律、教育、政策分析及公共道德论证领域，大语言模型（LLM）的输出常被用于需要以文本证据和明确规范标准为依据的解释性工作。然而一种反复出现的失效模式——笔者称之为“解释错位”——即模型生成的解读被当作确定的含义，却未明确解释框架（来源、范围约束、规范承诺）、未保留可辩护的替代方案，也未提供让读者能找到支撑段落的来源信息。在此类场景中，风险不仅在于事实错误，更在于问责机制的缺失：读者和机构无法可靠评估某一输出会让他们承担何种责任，或其依据是什么。本文借鉴哲学解释学，探讨了这一风险并推导了用于构建人机协同解释的设计原则；还对近期关于解释学与AI的学术成果进行了结构化综合，将这一新兴文献梳理为一系列反复出现的论证线索和与设计相关的缺口。本文将LLM输出视为候选解读，而解释学理解则归属于身处学科历史语言传统中的负责任人类解释者；人机交互被定义为一种AI介导的解释循环，解释学理解与基于令牌预测的文本生成相区分。在此基础上，现有LLM技术被重新组织为适用于解释场景中符合解释学负责任使用的设计模式。最后，本文探讨了其对法律实践、教育评估与反馈、学术知识生产及公共道德论证的启示，还将数字解释学视为一种素养：即通过检查框架、来源和解读，并对输出提出质疑，来阅读AI介导文本的能力。
英文摘要
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: readers and institutions cannot reliably assess what an output commits them to, or on what basis. Drawing on philosophical hermeneutics, this paper discusses this risk and derives design principles for structuring human-AI co-interpretation. The paper also provides a structured synthesis of recent scholarship on hermeneutics and AI, organizing this emerging literature into a set of recurrent lines of argument and design-relevant gaps. LLM outputs are treated as candidate readings, whereas hermeneutic understanding is reserved for accountable human interpreters situated in disciplinary historical-linguistic traditions. Human-AI interaction is characterized as an AI-mediated interpretive loop. Hermeneutic understanding is distinguished from token-prediction--based text generation. On this basis, existing LLM techniques are reorganized into design patterns for hermeneutically responsible use in interpretive settings. Finally, the discussion turns to implications for legal practice, educational assessment and feedback, scholarly knowledge production, and public moral argumentation. It also treats digital hermeneutics as a literacy: the capacity to read AI-mediated texts by examining frames, provenance, and readings, and by contesting outputs.
