A framework jointly models annotator-specific NLI labels and explanations using conditioned representations and two explainer architectures, improving predictive performance over baselines.
Inherent disagreements in human textual infer- ences.Transactions of the Association for Computational Linguistics, 7:677–694
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Introduces Defensibility Index, Ambiguity Index, and Probabilistic Defensibility Signal to evaluate AI moderation decisions by logical derivability from explicit rules rather than agreement with historical labels, with validation on 193k+ Reddit cases showing 33-46.6 pp metric gaps and a Governance
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Fine-Grained Perspectives: Modeling Explanations with Annotator-Specific Rationales
A framework jointly models annotator-specific NLI labels and explanations using conditioned representations and two explainer architectures, improving predictive performance over baselines.
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Introduces Defensibility Index, Ambiguity Index, and Probabilistic Defensibility Signal to evaluate AI moderation decisions by logical derivability from explicit rules rather than agreement with historical labels, with validation on 193k+ Reddit cases showing 33-46.6 pp metric gaps and a Governance