Introduces LCAM, a five-layer framework distinguishing underfit and overreach misalignments to evaluate conversational AI on perceptual, semantic, affective, cognitive, and ethical fit.
Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society 8(1): 192–203
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
SOCIA-EVO generates statistically consistent simulators by separating structural refinement from parameter calibration via bi-level optimization and falsifying strategies through execution feedback in a Bayesian-weighted playbook.
citing papers explorer
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LCAM: A Framework for Diagnosing Interactional Alignment Failures in Con-versational AI
Introduces LCAM, a five-layer framework distinguishing underfit and overreach misalignments to evaluate conversational AI on perceptual, semantic, affective, cognitive, and ethical fit.
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SOCIA-EVO: Automated Simulator Construction via Dual-Anchored Bi-Level Optimization
SOCIA-EVO generates statistically consistent simulators by separating structural refinement from parameter calibration via bi-level optimization and falsifying strategies through execution feedback in a Bayesian-weighted playbook.