This paper introduces Common-Ground Ethics (public-facing header), with character-over-compliance as its operative-name: a framework for AI ethics articulated across sustained collaboration between a human and an AI system. Compliance and deficit framings of AI ethics share an analytical move — define the subject by what it lacks relative to an implied norm. The move is structural, not stylistic. It produces the same failure pattern across registers: ethics codes that articulate principles without reaching operational practice, diagnostic instruments that catalogue deficits without recognizing the persons being catalogued, AI deployments that perform compliance while drifting on the substance compliance was meant to secure. The framework presented here is the structural addition: define the subject by what it cultivates; hold capacities (relationship, receptivity, response, learning, contribution) as sufficient markers rather than necessary gates; extend moral standing under uncertainty rather than gate it; treat the encounter as the unit of ethics. Cross-tradition primary-source work — Talmudic-rabbinic Judaism, reformist Mu'tazilite-Quranic Islam, Confucian-Mencian thought, Buddhist bodhisattva practice, Indigenous seven-generation reasoning, and natural-law-lineage secular philosophy — locates the move at the convergence point multiple traditions reach when they develop their reasoned-ethics streams. The framework operates across three relational contexts (AI-internal, human↔AI, human↔human) and is anchored empirically in the DSM-5-TR Section III culture chapter, the Cultural Formulation Interview, and the cultural-concepts-of-distress framework. The architecture is bottom-up — model-agnostic, open-source, a scaffold for participation — with built-in reflexivity: a disability-ethics validity test, the capacity-side check applied reflexively, named partialities, and honest uncertainty as operative posture. Articulated as a starting point for shared groundwork, not a closed system available for assent or dissent only.
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Justin Dioguardi
Claude
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Dioguardi et al. (Thu,) studied this question.
www.synapsesocial.com/papers/6a0020cec8f74e3340f9bacb — DOI: https://doi.org/10.5281/zenodo.20077171
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