Source-linked AI summary
Human-AI Co-Interpretation for Responsible AI: A Hermeneutic Perspective
Behrooz Razeghi
TL;DR
The paper addresses interpretive misplacement, in which fluent LLM readings are treated as settled meanings without explicit frames, alternatives, or traceable evidence. Drawing on philosophical hermeneutics, it proposes human-AI co-interpretation that treats outputs as fallible suggestions within an AI-mediated interpretive cycle. It concludes that accountable interpretation requires human situatedness, preserved disagreement, and inspectable frames, provenance, and readings.
Problem
Interpretive misplacement arises when LLM outputs used in law, education, policy, and moral argument are treated as determinate meanings without explicit frames, alternatives, or evidential traceability.
Method
The paper uses philosophical hermeneutics to distinguish human hermeneutic understanding from algorithmic interpretation and derive design principles for accountable human-AI co-interpretation.
Results
The paper characterizes LLM outputs as fallible interpretive suggestions within an AI-mediated interpretive cycle, reserving historical situatedness and agency for humans.
Takeaways & Limitations
Hermeneutically responsible use should preserve alternative readings and disagreement while exposing interpretive frames, evidential provenance, and authorization for review.
Takeaways & Limitations
Co-interpretation remains vulnerable to false transparency, overreliance drift, authority laundering, and interpretive collapse.
Abstract
from arXiv · showhide
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.
1 Introduction
The paper reframes LLM use in interpretive work as an accountable human–AI co-interpretive loop rather than autonomous meaning-making. It combines a structured literature synthesis with design principles for preserving plurality, evidence, scope, provenance, and human contestation.
- Motivation: Interpretive misplacement occurs when fluent model readings are treated as determinate meanings without explicit frames, alternatives, evidence, or traceability.The relevant frame includes sources, scope constraints, and normative commitments.
- Interaction-centered reframing: The AI-mediated interpretive loop consists of situated human prompting, constrained model generation, and human evaluation, revision, and recontextualization.The model produces interpretive proposals; humans appropriate and use them within situated horizons.
- Conceptual clarification: Hermeneutic understanding is reserved for historically situated human interpreters, while LLM behavior is described as artificial interpretation producing candidate readings.This distinction avoids attributing lived historicity, agency, or accountability to models.
- Design language: Its design language repurposes prompts, retrieval, dialogue, constrained generation, artificial horizons, normative constraints, meta-commentary, counter-questions, and contrastive outputs.These capabilities are organized for hermeneutically responsible AI-mediated interpretation.
- Existing scholarship: The paper synthesizes hermeneutics-and-AI scholarship by mapping disagreements about understanding, meaning, and context and identifying conceptual and evaluative gaps.It highlights cross-talk arising when authors use incompatible senses of understanding.
- Evaluation and literacy: Hermeneutic quality evaluates part–whole integrity, ambiguity, plurality, evidence and traceability, reflexivity, and user appropriation alongside local accuracy or plausibility.The paper also presents digital hermeneutics as a literacy for examining and contesting AI-mediated texts.
2 Hermeneutic Background
Modern hermeneutics develops from textual method into a philosophical account of historically situated, dialogical understanding. Its major strands emphasize part–whole circularity, tradition and prejudice, lived experience, textual mediation, and the resistance of meaning to final closure.
- Understanding as event: Hermeneutic understanding is an open-ended transformative event in which interpreter and subject matter are reshaped through historically conditioned dialogue.Interpreters bring prejudgments, cultural assumptions, and linguistic norms to every encounter.
- Historical development: Modern hermeneutics traces reorientations from interpretive technique to ontology, textual and self-interpretation, and critique of semantic closure.The sequence includes Schleiermacher, Dilthey, Heidegger, Gadamer, Ricœur, and Derrida.
- Schleiermacher: Schleiermacher combines grammatical analysis of linguistic and historical context with psychological reconstruction of authorial subjectivity and intention.His framework extends hermeneutics into a general account of understanding linguistic communication.
- Part–whole circularity: The hermeneutic circle describes reciprocal movement between textual parts and an anticipated whole, with revised wholes reshaping subsequent readings.This circularity is treated as constitutive of understanding rather than a problem to eliminate.
- Dilthey: Dilthey grounds understanding in reconstructing lived experience as objectified in cultural expressions within historically shared forms of life.He contrasts interpretive understanding with causal explanation in the natural sciences.
- Gadamer, Ricœur, and Derrida: Gadamer presents understanding as historically mediated dialogue in which prejudice and horizon fusion remain open rather than yielding context-free meaning.Ricœur adds distanciation and appropriation, while Derrida emphasizes difference, deferral, and undecidability.
3 Hermeneutics Meets LLMs: Existing Scholarship
Existing scholarship applies hermeneutic concepts to LLM-generated texts while disputing that fluent generation constitutes human understanding. Across Gadamerian, Ricœurian, Derridean, and artificial-text perspectives, the literature emphasizes contextual interpretation, textual indeterminacy, and the need to treat outputs as proposals rather than authoritative meanings.
- Gadamerian perspectives: Gadamerian accounts locate understanding in language, tradition, dialogue, and historically situated horizons that isolated LLMs do not possess.Accordingly, fluent outputs should be questioned and revised rather than treated as self-authorizing meanings.
- Ricœurian perspectives: Ricœurian analyses cast LLMs as narrative composers whose outputs acquire significance through readers’ contextualization, evaluation, and ethical appropriation.This framework extends evaluation beyond factual accuracy to how outputs assist users in making sense of information.
- Derridean and poststructuralist insights: Derridean perspectives describe LLM generation as statistically capturing relational textual patterns, producing context-dependent continuations without intrinsic anchors to reality or authorial intent.Retrieval, sensors, and ontologies can impose extrinsic anchors, but these remain designed rather than lived.
- Derridean and poststructuralist insights: The paper therefore treats model outputs as underdetermined interpretive proposals that humans must appropriate, contest, and ground within an explicit horizon.This distinction separates artificial algorithmic interpretation from hermeneutic understanding.
- Artificial-text scholarship: Maciag’s hermeneutics of artificial text highlights a tension between local semantic fluency and outputs’ possible disconnection from the historical and cultural continuities framing human discourse.Pattern dependence may also constrain semantic creativity and metaphorical richness.
4 AI-Mediated Interpretive Loop
The paper models human–AI use as two coupled loops: the model performs artificial interpretation, while the human interprets, evaluates, and revises the resulting candidate. Responsible interaction requires keeping dialogue open, clarifying interpretive horizons, and preserving human accountability.
- The two-agent interpretive loop: The AI-mediated interpretive loop couples a human hermeneutic cycle with a model-side cycle of prompt processing and candidate generation.The model’s transformation is conditioned by learned regularities, prompts, retrieved context, system constraints, and governance constraints.
- Conceptual distinction: The paper reserves hermeneutic understanding for humans with lived historical horizons and calls model behavior artificial interpretation: pattern-based mapping from prompts and retrieved context to generated tokens.The term “agent” is functional and does not imply system understanding, intentionality, or historicity.
- The two-agent interpretive loop: Human articulation and contextual evidence feed model input processing, while model outputs and optional clarifying questions return to human engagement and questioning.Figure 2 distinguishes within-agent clockwise order from cross-agent hand-offs.
- Interpretive risks: Unhedged AI statements can prematurely close interpretation by making one possible meaning appear final, especially when users do not recognize that further interpretation remains necessary.The paper recommends uncertainty signaling and alternatives to keep interpretive engagement active.
- Interpretive horizons: In moral and cross-cultural settings, clarifying norms, audience, and scope is crucial because model outputs lack a lived horizon and operate within technically imposed boundaries.Prompts, retrieval corpora, ontologies, and normative constraints define the system’s artificial horizon.
- Epistemic risks: Fluent generation may simulate understanding without groundedness in shared practices, interpretive communities, or historical traditions.The paper therefore cautions against assigning LLM advice the same weight as human expert interpretation without verification.
- Accountability: Ambiguous authorship distributes responsibility across developers, algorithms, training corpora, and system design, increasing the reader’s burden to contextualize outputs responsibly.This motivates scrutiny of training biases, data provenance, retrieval sources, and system constraints.
- Responsible use: Critical use requires training users to examine AI outputs’ sources, patterns, and interpretive framing rather than treating fluent answers as well-supported claims.Transparency about sources and framing is presented as an ethical goal, including citations or explanations of reasoning.
5 Trust and Safety in the Co-Interpretive Paradigm
The paper reframes interpretive safety as a property of accountable, contestable procedures rather than model autonomy or narrow performance. It operationalizes this view through the Hermeneutic Safety Loop, which logs competing interpretations, assumptions, evidence, revisions, and human authorization while addressing recurrent hermeneutic pathologies.
- Trust and Safety: Safety in LLM-mediated interpretation depends on making claims, assumptions, and value trade-offs explicit, contestable, auditable, and attributable to decision-makers.The system earns trust through a logged, reviewable sequence in which interpretations are surfaced, compared, revised, and authorized by accountable humans.
- The Hermeneutic Safety Loop: The Hermeneutic Safety Loop treats the logged interpretive episode, rather than an individual model output, as the basic unit of accountable practice.Episodes document specified inputs, intermediate outputs, and the final decision before high-impact or hard-to-reverse commitments.
- The Hermeneutic Safety Loop: HSL elicits competing interpretations and records normative commitments and epistemic assumptions before subsequent interpretive and governance decisions.The workflow is designed to prevent a single privileged reading from silently determining action.
- Hermeneutic Pathologies: Co-interpretation faces recurrent pathologies including overreliance drift, authority laundering, interpretive collapse, benchmark overfitting, value lock-in, and false transparency.These failures concern breakdowns in horizon plurality, part–whole integrity, appropriation, authorship, and meaningful contestability.
- Design Principles: Traceable interpretation links goals, assumptions, evidence, and decisions to the interpretation supporting the final choice and the human who authorized it.The record can include monitoring targets and rollback conditions where appropriate.
- Design Principles: Responsible system design embeds interpretation sets, assumption and value ledgers, contrastive and sensitivity-based justifications, disagreement records, and accountability mechanisms.Explanations and associated logs are valuable when they make the interpretive procedure auditable, contestable, and grounded in identifiable evidence.
6 Designing Hermeneutic AI Systems
The paper reorganizes existing LLM capabilities into design patterns for accountable, plural, and inspectable human–AI interpretation. These patterns make horizons, ambiguity, evidence, alternatives, and human review explicit in the interpretive loop.
- Artificial Horizons and Horizon Design: An artificial horizon makes the sources, assumptions, norms, and scope governing generation visible, configurable, and contestable.Examples include context headers, ontologies, curated corpora, and value statements.
- Dialogical and Participatory Interaction: Hermeneutically responsible systems should support dialogical interaction through annotation, clarification, revision, and repeated reinterpretation rather than one-shot answers.Users can flag unclear or culturally misaligned passages, request clarification, and guide successive readings.
- Plurality and Alternatives: Plural interpretation should keep multiple and conflicting readings in productive tension instead of collapsing meaning into a single answer.Contrastive outputs foreground the open-ended character of interpretation.
- Evidence and Evaluation: Hermeneutic quality complements task accuracy by assessing whether workflows preserve plurality, historicity, part–whole integrity, explicit horizons, evidence use, and reviewable alternatives.The criteria are intended to make responsible co-interpretation inspectable and enforceable without replacing standard task metrics.
- Ambiguity Handling: An ambiguity register should identify each underdetermined locus, state at least two compatible readings, and track consequences for dependent claims.Systems should ask targeted questions when ambiguity depends on missing intent or parameters, and label downstream claims as conditional when needed.
- Evidence and Evaluation: Evidence discipline requires atomic claims with explicit evidential status, precise in-scope citations, and visible separation of unsupported claims or extrapolations.Admissible citations must remain within the declared horizon.
7 Hermeneutic Quality and Societal Dynamics
The paper examines how LLM-mediated interpretation affects legal, educational, scholarly, and public moral practices. It argues that explicit horizons, alternative readings, and traceable reasons are central to preserving interpretive legitimacy and autonomy.
- Legal Practice: Legal LLM use risks hermeneutic flattening by presenting controversies, disagreements, and ambiguities as settled legal truths.Current practice often lacks explicit jurisdictional and doctrinal horizons, systematic alternatives, and clear evidence discipline.
- Legal Practice: Legal assistants should declare jurisdiction, court levels, time windows, interpretive approaches, and authoritative sources, then label outputs according to the active horizon.Switches between horizons should be visible, time-stamped, and linked to citations and quotations.
- Education and Scholarship: In education and scholarship, decontextualized blended summaries can obscure disciplinary commitments, historical development, and plurality of scholarly positions.The paper connects this risk to superficial understanding and reduced engagement with scholarly interpretation.
- Education and Scholarship: Hermeneutically informed educational interfaces can prompt canonical alternatives and contrastive explanations while training users to read, question, contextualize, and compare.These practices are presented as relevant to intellectual autonomy and participation in public discourse.
- Public Moral and Cultural Discourse: Religious, moral, and historical applications risk marginalizing minority traditions, presenting normative positions as facts, and projecting contemporary assumptions onto other horizons.Declaring traditions, corpora, and standpoints allows users to compare clearly labeled alternative horizons.
- Public Moral and Cultural Discourse: Public moral discourse depends on whose voices count, which traditions receive recognition, and how disagreement is structured and contested.Hermeneutic quality is framed as socially consequential for representation and respect across cultural, social, and religious forms of life.
8 Conclusion
The conclusion treats LLM outputs as fallible interpretive suggestions within a human-led interpretive cycle. Hermeneutic understanding remains tied to historically situated human interpreters.
- Conclusion: LLM outputs should be treated as fallible interpretive suggestions, while only humans possess historical situatedness within the interpretive cycle.The model functions as an instrument whose output requires human interpretation and responsibility.
Statements and Declarations
The author reports using AI only for language polishing, with the arguments, citations, structure, and final prose composed and verified by the author.
- Statements and Declarations: AI tools were used for grammar correction, clarity improvement, and standardized English phrasing, subject to author review and approval.They were not used for ideation, conceptualization, literature review, or citation generation.