A Four-Phase Architecture for Ethical Observability in AI Decision Systems

This project presents a four-phase decision architecture for ethical observability in AI systems, accompanied by standalone testing and evaluation packets for each phase. The work is structured to support independent critique, replication, and falsification. Each phase includes a reader orientation document and a formal evaluation packet defining failure modes, rejection criteria, and adversarial stress tests. The framework emphasizes artifact-level observability over decision correctness and explicitly avoids intervention in runtime behavior. Governance mechanisms, where defined, are observational and non-delegative by design. All materials are provided in full to support transparency, experimental evaluation, and disagreement.