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Détails bibliographiques
Auteur principal:	Gough, Timothy M.
Format:	Recurso digital
Langue:
Publié:	Zenodo 2025
Sujets:	deterministic systems ethical ai human-aligned ethics state transition law ethical admissibility bounded evolution ai ethics sequential decision systems stability analysis normative invariance operating sytems governance ai governance Deterministic decision systems Ethical admissibility Invariant sets Bounded autonomy Ethics architecture State-space geometry Internal representations Drift prevention Adversarial robustness Safety-critical AI Hybrid decision systems Cognitive stability Institutional coherence Machine alignment Ethical constraints Formal methods Autonomous governance Constraint-based AI State evolution Input-state-rule mapping Ethical contraint E Dynamic systems theory Constraint topologies AI safety framework Deterministic intelligence Internal state dynamics Coherent evolution Mathematical ethics State-bounded reasoning Normative geometry Interpretability framework Structurally aligned systems Decision architecture mathmatical truth Counterfactual reasoning Ethics-embedded computation Structural alignment Topological constraints Stability under perturbation Predictive consistency Ethics-based invariance Forbidden state space Deterministic transition mapping truth scoring Universal ethical primitives daios DUM Constraint-driven evolution Normative attractors Deterministic governance Artificial intelligence Artificial satellite Artificial Intelligence Pacemaker, Artificial Artificial Intelligence/economics Artificial Intelligence/standards Artificial Intelligence/trends Artificial Intelligence/classification Artificial Intelligence/ethics Artificial Intelligence/history Artificial Intelligence/supply & distribution Artificial Intelligence/legislation & jurisprudence Artificial Intelligence/statistics & numerical data Machine learning Machine Learning Supervised Machine Learning Machine Learning/classification Machine Learning/ethics Machine Learning/standards Machine Learning/trends Supervised Machine Learning/standards Machine Learning/legislation & jurisprudence Supervised Machine Learning/ethics Unsupervised Machine Learning/ethics Ethical Review/legislation & jurisprudence critical ethics
Accès en ligne:	https://doi.org/10.5281/zenodo.17826047
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This white paper introduces Deterministic Ethics-Constrained State Transition Law, a universal framework that formalizes how ethical admissibility must be embedded directly within the state-transition function of any human or machine decision system. The law establishes the ethical boundary E as a structural invariant governing which successor states a system is permitted to enter, resolving failure modes found across cognition, autonomy, governance, and machine learning. This work is the lead paper in a unified deterministic intelligence framework. Two companion papers—The Deterministic Unification Model and DAIOS: The Deterministic AI Operating System—provide the structural and computational foundations that this ethical law completes. Together, the trilogy defines the first universal architecture for stable, transparent, and ethically bounded decision systems across human, institutional, and artificial domains.

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