When Does an Interpretation Count as Established? The Formation, Evaluation, and Responsibility of Interpretation in Generative AI
- URL: http://arxiv.org/abs/2609.04766v1
- Date: Fri, 04 Sep 2026 05:58:41 GMT
- Title: When Does an Interpretation Count as Established? The Formation, Evaluation, and Responsibility of Interpretation in Generative AI
- Abstract summary: The paper asks how an interpretation comes to be recognized within sociotechnical processes.<n>It proposes five public requirements concerning materials and versions, evidential roles, failure, contract revision, and responsibility.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Generative AI research has increasingly evaluated factuality, citation, coverage, and report structure. Yet passing such local checks does not by itself show that a humanistic interpretation has been established. This paper asks how an interpretation comes to be recognized within sociotechnical processes. It introduces three connected concepts. Interpretive appearance names the gap between the finished form of an output and the publicly traceable process through which materials, counterevidence, and revisions constrained the judgment. The evaluation contract names the bounded materials, tasks, criteria, permitted inferences, and failure conditions within which a local judgment is valid. Standing substitution names the unwarranted conversion of a genuine local pass into a stronger claim that an interpretation, result, or research capability has been established, without commensurate new evidence or bridging arguments. The paper then examines responsibility for judgment: a text may acquire recognition while no public structure remains for stating reasons, answering objections, revising, downgrading, or withdrawing the conclusion. Humanistic scholarship provides a revealing test because new materials and conceptual distinctions can alter both the question and the criteria of evaluation. The paper therefore develops delayed closure as a practice of keeping recognized interpretations revisable and proposes five public requirements concerning materials and versions, evidential roles, failure, contract revision, and responsibility. The argument is conceptual and normative: it does not claim to offer a benchmark or to determine whether models possess understanding. It instead explains why local evaluation, finished textual form, and public recognition must not be treated as sufficient evidence that an interpretation has been formed.
Related papers
- Autoformalizing Argumentative Material Inferences [26.35292656634472]
We develop an autoformalization for argumentative material inference as guard completion.<n>GUARD yields significant improvements in verified-faithful.<n>We show that the symbolic soft critique and the explicit assumption layer account for most of these gains.
arXiv Detail & Related papers (2026-09-15T11:05:26Z) - What Reaches Expert Review? Representation, Structural Screening, and Candidate-Form Dependence in AI-Assisted Item Development [0.7834978654423369]
representation, structural reduction, and selection policy determine which items and evidence psychometricians ever receive.<n>Across two linked in-silico studies of 32,000 selected Big Five items, we followed fixed source populations from semantic representation through structural evaluation and candidate-form construction.
arXiv Detail & Related papers (2026-08-24T18:57:13Z) - Human-AI Co-Interpretation for Responsible AI: A Hermeneutic Perspective [4.832750949021042]
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.
arXiv Detail & Related papers (2026-08-02T18:19:13Z) - When benchmark inferences do not compose: Projectibility in AI evaluation [0.0]
An AI benchmark result rarely reaches a consequential claim in one step.<n>Evaluators generalize it to further cases, interpret it as evidence of capability, extrapolate it to new tasks, transport it to another system or site.<n>This paper identifies a further problem: warranted links don't automatically make a warranted chain.
arXiv Detail & Related papers (2026-07-28T18:07:04Z) - Definitional alignment before capability alignment: a Design-Science framework for adjudicating claims about AGI [0.0]
"AGI" lacks a single shared and stable referent and competing operationalizations can return different verdicts on the same system.<n>This article treats that under-specification as a design and governance problem.
arXiv Detail & Related papers (2026-06-10T22:04:56Z) - Beyond Probabilistic Similarity: Structural, Temporal, and Causal Limitations of Retrieval-Augmented Generation in the Legal Domain [0.0]
Retrieval-Augmented Generation has become a standard architectural response to unreliability in legal AI.<n>We argue that these failures are symptoms of an architectural mismatch between probabilistic retrieval and the hierarchical, temporal, and institutional structure of legal knowledge.<n>We develop the argument in three moves. First, we articulate the ontological commitment of legal knowledge as a triad of properties derivable from classical legal theory.<n>Second, we identify three corresponding pathologies of retrieval (mereological blindness, diachronic blindness, and causal opacity), each developed with an operational definition, a failure mechanism, a canonical example, and detection criteria for
arXiv Detail & Related papers (2026-06-08T16:46:53Z) - When AI reviews science: Can we trust the referee? [73.47745294608072]
We map attacks across the review lifecycle -- training and data retrieval, desk review, deep review, rebuttal, and system-level.<n>We use two advanced LLM-based referees to isolate the causal effects of prestige framing, assertion strength, rebuttal sycophancy, and contextual poisoning on review scores.
arXiv Detail & Related papers (2026-04-26T08:03:32Z) - FactReview: Evidence-Grounded Reviews with Literature Positioning and Execution-Based Claim Verification [57.196748998757954]
We present FactReview, an evidence-grounded reviewing system that combines claim extraction, literature positioning, and execution-based claim verification.<n>Given a submission, FactReview identifies major claims and reported results, retrieves nearby work to clarify the paper's technical position, and, when code is available, executes the released repository.<n>It then produces a concise review and an evidence report that assigns each major claim one of five labels: Supported, Supported by the paper, Partially supported, In conflict, or Inconclusive.
arXiv Detail & Related papers (2026-04-05T11:45:22Z) - 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) - Is the Top Still Spinning? Evaluating Subjectivity in Narrative Understanding [40.78154629252038]
Forcing binary labels upon ambiguous claims lowers the reliability of evaluation.<n>We introduce LLM-generated edits of summaries as a method of providing a nuanced evaluation of claims.<n>We show that ARM produces a absolute 21% improvement in annotator agreement on claim faithfulness.
arXiv Detail & Related papers (2025-04-01T19:08:24Z) - DnDScore: Decontextualization and Decomposition for Factuality Verification in Long-Form Text Generation [48.134780006638984]
decomposition and decontextualization have been explored independently, but their interactions in a complete system have not been investigated.<n>We conduct an evaluation of different decomposition, decontextualization, and verification strategies and find that the choice of strategy matters in the resulting factuality scores.<n>We introduce DnDScore, a decontextualization aware verification method which validates subclaims in the context of contextual information.
arXiv Detail & Related papers (2024-12-17T18:54:01Z) - DELTA: Pre-train a Discriminative Encoder for Legal Case Retrieval via Structural Word Alignment [55.91429725404988]
We introduce DELTA, a discriminative model designed for legal case retrieval.
We leverage shallow decoders to create information bottlenecks, aiming to enhance the representation ability.
Our approach can outperform existing state-of-the-art methods in legal case retrieval.
arXiv Detail & Related papers (2024-03-27T10:40:14Z) - 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.