Blockchain-Anchored Audit Trails for Neuro-Symbolic Urban AI: Immutable Accountability for Autonomous Decisions in Smart City Infrastructure

Abstract

Autonomous AI systems making real-time decisions over traffic management, energy dispatch, and emergency services in smart city infrastructure exert direct and consequential influence over citizen welfare, yet current smart city AI architectures provide no tamper-proof record of what the system decided, which rules or neural pathways drove the decision, and whether the decision record has been modified after the fact. This paper proposes a blockchain-anchored audit trail architecture for neuro-symbolic urban AI, comprising seven interoperable smart contracts deployed on a permissioned blockchain that capture every autonomous decision produced by a neuro-symbolic smart city AI as a structured, hash-chained, publicly auditable on-chain event. The neuro-symbolic AI architecture, which combines symbolic safety rule enforcement with neural perception to produce interpretable multi-domain urban decisions, generates structured decision records encoding the symbolic rules invoked, the neural confidence scores, the sensor input state, and the action taken records that are substantive enough for blockchain commitment to support genuine accountability rather than opaque hash logging. The seven smart contracts implement decision submission, symbolic rule version hashing, neural-symbolic conflict detection, and governance voting for rule amendments, citizen decision trace queries, counterfactual incident replay, and cryptographically signed regulatory audit export. The architecture satisfies the EU AI Act Article 19 mandatory logging requirements for high-risk AI systems, the GDPR Article 22 right to explanation for automated decisions, and the NIST AI Risk Management Framework governance function, while providing a citizen challenge mechanism and a tamper-proof governance record that centralized smart city AI architectures cannot offer by design.