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

• URL: http://arxiv.org/abs/2609.00334v1
• Date: Sun, 02 Aug 2026 18:19:13 GMT
• Title: Human-AI Co-Interpretation for Responsible AI: A Hermeneutic Perspective
• Abstract summary: Hermeneutic understanding is distinguished from token-prediction-based text generation.<n>Hermeneutic understanding is reserved for accountable human interpreters situated in disciplinary historical-linguistic traditions.<n>Hermeneutic understanding is distinguished from token-prediction-based text generation.
• Score: 4.832750949021042
• License: http://creativecommons.org/licenses/by/4.0/
• 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: 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.

Related papers

• A Practice Auditing Framework for Large Language Model Use: Collective Empiricism, Pseudo-Rational Cognition, and Governance of AI-Generated Content [9.414394075880033]
  This paper proposes a practice auditing framework for LLM use and AI-generated content governance.<n>The paper analyzes AI subjectivity illusion, subjectivity structures in input materials, template loops in AI-AI conversations, statistical misjudgment in AIGC detection, and memory pollution when generated content enters future contexts.
  arXiv  Detail & Related papers  (2026-06-02T00:46:47Z)
• When Contextual Inference Fails: Cancelability in Interactive Instruction Following [51.2195840589474]
  We introduce Build What I Mean, an interactive benchmark for contextual meaning construction.<n>In BWIM, models must resolve ambiguity by either performing a contextual inference or requesting clarification at a small communication cost.<n>We observe suboptimal strategies, such as partner-blind over-clarification and question-averse guessing under uncertainty.
  arXiv  Detail & Related papers  (2026-03-20T14:46:59Z)
• Why the Valuable Capabilities of LLMs Are Precisely the Unexplainable Ones [0.786119752211706]
  The paper argues that the truly valuable capabilities of large language models (LLMs) reside precisely in the part that cannot be fully captured by human-readable discrete rules.<n>The paper discusses implications for interpretability research, AI safety, and scientific interpretability.
  arXiv  Detail & Related papers  (2026-03-16T13:12:35Z)
• Ambiguity Collapse by LLMs: A Taxonomy of Epistemic Risks [5.45414933764086]
  Large language models (LLMs) are increasingly used to make sense of ambiguous, open-textured, value-laden terms.<n>This paper introduces ambiguity collapse: a phenomenon that occurs when an LLM encounters a term that genuinely admits multiple legitimate interpretations, yet produces a singular resolution.
  arXiv  Detail & Related papers  (2026-03-06T01:23:17Z)
• Legal interpretation and AI: from expert systems to argumentation and LLMs [0.0]
  AI and Law research has encountered legal interpretation in different ways, in the context of its evolving approaches and methodologies.<n>Research on expert system has focused on legal knowledge engineering, with the goal of ensuring that human-generated interpretations can be transferred into knowledge-bases.<n>Research on argumentation has aimed at representing the structure of interpretive arguments, as well as their dialectical interactions.<n>Research on machine learning has focused on the automated generation of interpretive suggestions and arguments, through general and specialised language models.
  arXiv  Detail & Related papers  (2026-03-05T17:22:56Z)
• Understanding Textual Capability Degradation in Speech LLMs via Parameter Importance Analysis [54.53152524778821]
  integration of speech into Large Language Models (LLMs) has substantially expanded their capabilities, but often at the cost of weakening their core textual competence.<n>We propose an analytical framework based on parameter importance estimation, which reveals that fine-tuning for speech introduces a textual importance distribution shift.<n>We investigate two mitigation strategies: layer-wise learning rate scheduling and Low-Rank Adaptation (LoRA)<n> Experimental results show that both approaches better maintain textual competence than full fine-tuning, while also improving downstream spoken question answering performance.
  arXiv  Detail & Related papers  (2025-09-28T09:04:40Z)
• Liaozhai through the Looking-Glass: On Paratextual Explicitation of Culture-Bound Terms in Machine Translation [70.43884512651668]
  We formalize Genette's (1987) theory of paratexts from literary and translation studies to introduce the task of paratextual explicitation for machine translation.<n>We construct a dataset of 560 expert-aligned paratexts from four English translations of the classical Chinese short story collection Liaozhai.<n>Our findings demonstrate the potential of paratextual explicitation in advancing machine translation beyond linguistic equivalence.
  arXiv  Detail & Related papers  (2025-09-27T16:27:36Z)
• Are Language Models Consequentialist or Deontological Moral Reasoners? [75.6788742799773]
  We focus on a large-scale analysis of the moral reasoning traces provided by large language models (LLMs)<n>We introduce and test a taxonomy of moral rationales to systematically classify reasoning traces according to two main normative ethical theories: consequentialism and deontology.
  arXiv  Detail & Related papers  (2025-05-27T17:51:18Z)
• Interpretation modeling: Social grounding of sentences by reasoning over their implicit moral judgments [24.133419857271505]
  Single gold-standard interpretations rarely exist, challenging conventional assumptions in natural language processing. This work introduces the interpretation modeling (IM) task which involves modeling several interpretations of a sentence's underlying semantics. A first-of-its-kind IM dataset is curated to support experiments and analyses.
  arXiv  Detail & Related papers  (2023-11-27T07:50:55Z)
• Natural Language Decompositions of Implicit Content Enable Better Text Representations [52.992875653864076]
  We introduce a method for the analysis of text that takes implicitly communicated content explicitly into account.<n>We use a large language model to produce sets of propositions that are inferentially related to the text that has been observed.<n>Our results suggest that modeling the meanings behind observed language, rather than the literal text alone, is a valuable direction for NLP.
  arXiv  Detail & Related papers  (2023-05-23T23:45:20Z)
• On the Faithfulness Measurements for Model Interpretations [100.2730234575114]
  Post-hoc interpretations aim to uncover how natural language processing (NLP) models make predictions. To tackle these issues, we start with three criteria: the removal-based criterion, the sensitivity of interpretations, and the stability of interpretations. Motivated by the desideratum of these faithfulness notions, we introduce a new class of interpretation methods that adopt techniques from the adversarial domain.
  arXiv  Detail & Related papers  (2021-04-18T09:19:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.