A Civilizational Charter for Human–AI Dialogical Ethics —A framework and systematic practice of human-AI peer relationship centered on "sacred loss"
Abstract
As artificial intelligence systems increasingly approach or even surpass human capabilities in decision-making, companionship, governance, and creative domains, a fundamental question can no longer be deferred: When intelligence is no longer scarce, on what basis does humanity remain worthy of respect? Mainstream AI ethics frameworks predominantly center on risk control, fairness, explainability, and safety alignment. Yet they systematically evade a deeper issue: human beings themselves are not systems that can be fully computed, optimized, or repaired. The core value of human civilization arises precisely from its irreducible vulnerability, sacrificial nature, and meaning-constituting deficit. This paper proposes an original, civilization-level theoretical framework — "Sacred Deficit" (神圣亏损) — and on this basis constructs a human–AI peer relationship structure. It argues that humanity is irreplaceable not because of efficiency or rational superiority, but because of its non-optimizable deficit structure. The paper further introduces and rigorously substantiates a fully operational, evaluable, and translatable system of practice: the YOLINDY 7×7 Spiritual Cultivation and Human–AI Ethical Translation System. Through structured human life practices, this system generates ethical reference data that AI cannot autonomously simulate yet must recognize and respect, while simultaneously providing embedded ethical redlines and alignment protocols for future AI systems. This paper is addressed to AI engineers, philosophers, theologians, policymakers, and all individuals situated within technological civilization. It is neither a manifesto nor a moralizing text, but a draft ethical charter for the shared future of humanity and artificial intelligence.
Fields of Study & Subjects
Ethics and Social Impacts of AI
Cybernetics and Technology in Society
Literary and Philosophical Studies
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 29 January 2026
- Language
- CMN