Ethical AI Design in Blockchain-Powered Surveillance Systems

Abstract

Artificial intelligence (AI) and distributed ledger technologies are increasingly integrated into public and private surveillance infrastructures—from city-wide camera networks to critical-infrastructure monitoring and access control. This integration promises higher integrity and accountability through immutable logs, faster incident response via on-device inference, and interoperable audit trails across organizations. Yet it also amplifies ethical risks: mass data collection, opacity in model decisions, function creep, demographic harms, cross-border data governance conflicts, and accountability gaps when immutable records meet “right to erasure” regimes. This manuscript proposes an ethics-by-design reference architecture for blockchain-powered surveillance that embeds privacy, proportionality, and fairness controls into each lifecycle stage (purpose definition → data capture → model training → inference → access → audit → decommissioning). Technically, it composes privacy-enhancing technologies (PETs)—including differential privacy, federated learning, zero-knowledge proofs, verifiable credentials (VCs), and content-provenance standards (C2PA)—with permissioned blockchain ledgers, model cards, and risk management aligned to the NIST AI RMF, ISO/IEC 23894, ISO/IEC 42001, UNESCO, and ACM guidance. A simulated evaluation illustrates how the architecture can reduce false-positive disparities and unauthorized access, while preserving evidentiary integrity. We discuss tensions with GDPR (e.g., Article 17 erasure; DPIA obligations), constraints introduced by the EU AI Act (e.g., prohibitions and high-risk biometric uses), and strategies to reconcile immutability with privacy (e.g., off-chain storage with revocation, redaction-friendly commitments). The paper closes with limitations and a future research agenda for measurable, auditable ethical guarantees in real-time surveillance.