Constitutionalising algorithmic enforcement

This chapter offers a clear, comparative map of algorithmic enforcement in the IP domain, with emphasis on copyright and trade marks online. In short, the chapter argues for a constitutional settlement for automated IP enforcement. It begins by defining the operational toolkit—fingerprinting, hashing, classifiers, watermarking, demotion and related workflows. It treats filters and ranking systems not as inevitable black boxes but as governable institutions subject to rights-first limits, measurable performance, and public oversight. It then traces the voluntary, judicial and legislative drivers of today’s regime across multiple jurisdictions, integrating doctrinal developments with empirical evidence and law-and-economics insights to explain how incentives, error costs and market structure shape enforcement in practice. It shows how co-regulatory architectures (from DSA audits and researcher access to Ofcom’s codes) can convert private ordering into verifiable due process: clear reasons, explainability, appeal and human review, standardised accuracy metrics, auditable logs, and secure external testing. The core of the chapter tests these systems against fundamental-rights standards—due process, freedom of information and expression, privacy/data protection, and the freedom to conduct a business—using a proportionality and fair-balance frame that travels across jurisdictions. It closes with a policy-ready blueprint of ‘digital due process’ safeguards designed to protect rights and preserve contestability while keeping enforcement effective at scale. It also advances an incentive-compatible proposal—an algorithmic safe harbour conditioned on verified compliance—so effectiveness at scale is rewarded only when safeguards are real. The destination is pragmatic rather than utopian: an enforcement ecosystem that protects IP, preserves contestability for users and creators, and holds platforms to public-law-like duties, with empirical evaluation and iterative correction built in.