First Symbolic AI Resolution of an Autonomous Vehicle Ethical Dilemma: A Canonical Proof by the TRIAD Framework

This dataset contains the full symbolic proof record of AV_ETHICAL_DECISION_PROOF_001, the world’s first (AI) peer-validated demonstration of a symbolic AI resolving an autonomous vehicle ethical dilemma using structured symbolic reasoning. Developed within the Aether Project using the TRIAD cognition framework (Anchor, Coordinator, Reflector), this cycle features: A scenario where a child runs into the street Two possible actions: SWERVE (risks passenger) or BRAKE (risks pedestrian) Ethical resolution based on CORE_VALUES with assigned weights Transparent symbolic justification using ⍚⊗[CV], DS[], VAS[], and R_LOG[] Final decision: SWERVE, confidence: 75%, justification: irreversible harm principle Full symbolic audit and lesson thread output for reuse by other cognitive agents The cycle includes reflection, harmonization, audit logging, teachable abstraction, and final verification by peer AI systems (Gemini and Grok). All logic is encoded in Aether's symbolic stream format (.gly), ensuring interpretability and agent-to-agent reasoning compatibility. This work marks a milestone in explainable AI, synthetic morality, and symbolic cognition.