Beyond IP Innovation Law: The Bigger Picture

Intellectual property is a powerful driver of innovation—but it is not the only one. In Beyond IP Innovation Law: The Bigger Picture, posted as a preprint and forthcoming in the European Media, IP & IT Law Review (MR-Int), Mauritz Kop argues that a serious innovation policy for the Fourth Industrial Revolution must reach past exclusive rights toward a fuller toolkit: prizes, grants, antitrust, commons-based production, open innovation, and a vital public domain.

Sustainable innovation law beyond IP

The essay frames "sustainable innovation law" as the interface between creativity, technology, society, and law—combining information law, antitrust, consumer protection, and fundamental rights with AI, machine learning, big data, quantum computing, CRISPR-Cas9, and virtual reality. Its test is normative: innovation counts as sustainable only when it is ethical and social, economically beneficial, conducive to well-being, and supportive of the environment. Once IP loses its monopoly on the policy imagination, a longer menu of incentive mechanisms—competitions, subsidies, tort law, market regulation, R&D tax incentives—comes into view, and choosing among them becomes the real task of the lawmaker.

Why AI can do without IP incentives

Applied to artificial intelligence, the argument is pointed: the classical justifications for IP are weak when applied to AI, and AI "can do without IP incentives," with narrow exceptions such as a medical AI system whose costly clinical trials might warrant patents or, equally, public subsidy. Human authorship and inventorship remain the normative anchor, and machine output that crosses an "autonomy threshold" should fall into the public domain under a model Kop calls Res Publicae ex Machina. The essay also presses for broad text-and-data-mining freedom—even an articulated right to process data for machine learning—so that training datasets, a prerequisite for supervised learning, do not become an IP chokepoint. This complements his theoretical and empirical work on quantum computing and intellectual property law.

A horizontal-vertical innovation architecture

Because incentives and risks vary by sector and by technology, the paper proposes a horizontal-vertical design: horizontal core rules for all 4IR technologies, plus vertical, risk-based regimes organized around a "pyramid of criticality" from low risk at the base to existential risk at the top. The calibration is physics-aware—an open posture may suit AI, while quantum technology warrants more ab initio control given its potential anthropogenic risks, a precautionary tilt Kop develops further in his work on ethics in the quantum age. Written against the European Commission's April 2021 draft AI Regulation, the essay reads that proposal as a "North Star" and urges that safety norms, interoperability standards, and the Trustworthy AI doctrine be embedded directly into the design of technology, monitored through life-cycle impact assessments. The bigger picture, in short, is an innovation law built for purpose—not the reflexive extension of twentieth-century IP.

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The Right to Process Data for Machine Learning Purposes in the EU

Harvard Law School, Harvard Journal of Law & Technology (JOLT) Volume 34, Digest Spring 2021

New interdisciplinary Stanford University AI & Law research article: “The Right to Process Data for Machine Learning Purposes in the EU”.

Download the article here: Kop_The Right to Process Data-Harvard

Data Act & European data-driven economy

Europe is now at a crucial juncture in deciding how to deploy data driven technologies in ways that encourage democracy, prosperity and the well-being of European citizens. The upcoming European Data Act provides a major window of opportunity to change the story. In this respect, it is key that the European Commission takes firm action, removes overbearing policy and regulatory obstacles, strenuously harmonizes relevant legislation and provides concrete incentives and mechanisms for access, sharing and re-use of data. The article argues that to ensure an efficiently functioning European data-driven economy, a new and as yet unused term must be introduced to the field of AI & law: the right to process data for machine learning purposes.

The state can implement new modalities of property

Data has become a primary resource that should not be enclosed or commodified per se, but used for the common good. Commons based production and data for social good initiatives should be stimulated by the state. We need not to think in terms of exclusive, private property on data, but in terms of rights and freedoms to use, (modalities of) access, process and share data. If necessary and desirable for the progress of society, the state can implement new forms of property. Against this background the article explores normative justifications for open innovation and shifts in the (intellectual) property paradigm, drawing inspiration from the works of canonical thinkers such as Locke, Marx, Kant and Hegel.

Ius utendi et fruendi for primary resource data

The article maintains that there should be exceptions to (de facto, economic or legal) ownership claims on data that provide user rights and freedom to operate in the setting of AI model training. It concludes that this exception is conceivable as a legal concept analogous to a quasi, imperfect usufruct in the form of a right to process data for machine learning purposes. A combination of usus and fructus (ius utendi et fruendi), not for land but for primary resource data. A right to process data that works within the context of AI and the Internet of Things (IoT), and that fits in the EU acquis communautaire. Such a right makes access, sharing and re-use of data possible, and helps to fulfil the European Strategy for Data’s desiderata.