Ethics of AI-Driven Surveillance with Immutable Data Logs

Abstract

AI-driven surveillance systems—combining computer vision, sensor fusion, and predictive analytics—are rapidly diffusing into public safety, workplace monitoring, retail analytics, and critical infrastructure. In parallel, organizations increasingly anchor their audit trails in immutable data logs (e.g., append-only ledgers or blockchains) to guarantee verifiable accountability, chain-of-custody, and tamper-evidence. This paper examines the ethical terrain at that intersection. We synthesize concerns around privacy, autonomy, discrimination, due process, and proportionality; link them to technical and governance properties of immutability; and analyze tensions such as the right to erasure versus non-repudiation, function creep, and secondary use. Methodologically, we pair a conceptual-normative analysis with a simulation of an AI surveillance pipeline that incorporates immutable logging, differential privacy for analytics, and an “audit-trigger” that gates model updates. Statistical analysis on simulated events (N ≈ 1,000,000 over 30 days) suggests that immutable logs—when combined with targeted audits—can reduce false-positive disparities between demographic cohorts while improving investigative traceability. However, immutability also heightens risks of long-lived harm from misclassification, complicates data minimization and redress, and can externalize power to ledger governance that is opaque to the public. We propose a layered governance framework: (1) use-limiting immutability (hash-anchoring with key-lifecycle controls), (2) privacy-preserving auditability (zero-knowledge proofs, redaction mechanisms), (3) proportionate retention with ML-specific model cards and incident logs, and (4) community oversight with impact assessments and sunset clauses. The results underscore that immutable logging is neither intrinsically ethical nor unethical; its legitimacy depends on design choices, procedural safeguards, and distribution of accountability across institutions and communities.