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Current approaches to human-AI interaction often rely on anthropomorphic mimicry, risking ethical miscalibration and relational confusion. We propose a socioaffective framework for structuring human-AI relationships that prioritizes role-based integration, relational clarity, and adaptive trust calibration. Grounded in a family unit model, this framework enables AI systems to engage in ethical reasoning, emotional responsiveness, and context-sensitive prioritization without requiring human-like mimicry. We introduce the Contextual Overlay for Responsiveness and Adaptation (CORA) simulation architecture, which operationalizes this approach through dynamic valence parsing, adaptive prioritization, and fairness-preserving moral reasoning. Simulation results demonstrate CORA’s capacity to maintain relational coherence and ethical stability under complex emotional and logistical strain. This work offers a structural alternative to anthropomorphic design, advancing the development of AI systems capable of participating in meaningful social roles while preserving ethical transparency and trust calibration in human-AI interaction.
Turner et al. (Mon,) studied this question.