Innovation, Quantum-AI Technology & Law

Blog over Kunstmatige Intelligentie, Quantum, Deep Learning, Blockchain en Big Data Law

Blog over juridische, sociale, ethische en policy aspecten van Kunstmatige Intelligentie, Quantum Computing, Sensing & Communication, Augmented Reality en Robotica, Big Data Wetgeving en Machine Learning Regelgeving. Kennisartikelen inzake de EU AI Act, de Data Governance Act, cloud computing, algoritmes, privacy, virtual reality, blockchain, robotlaw, smart contracts, informatierecht, ICT contracten, online platforms, apps en tools. Europese regels, auteursrecht, chipsrecht, databankrechten en juridische diensten AI recht.

Berichten met de tag Res Publicae ex Machina
Public Property from the Machine published in Harmonizing Intellectual Property Law for a Trans-Atlantic Knowledge Economy

Brill | Nijhoff's edited volume Harmonizing Intellectual Property Law for a Trans-Atlantic Knowledge Economy—edited by Péter Mezei, Hannibal Travis, and Anett Pogácsás, with a foreword by Maciej Szpunar—includes a chapter by Mauritz Kop, founder of the Stanford Center for Responsible Quantum Technology, titled Public Property from the Machine. The chapter confronts the question generative artificial intelligence forces on intellectual property law: who should own what a machine makes when no human authored it.

A new category, not a new right

Kop's answer breaks with the reflex to meet new output with new ownership. He argues that human authorship and inventorship remain the normative basis of copyright and patent law, and that—on the chapter's account—extending those rights to fully AI-generated works would chill innovation, narrow cultural diversity, and crowd the commons. In their place he proposes Res Publicae ex Machina—public property from the machine—a deliberately designed, permission-free public-domain regime for creations and inventions that have crossed the autonomy threshold, the point at which output is produced without meaningful human creative contribution. He frames the move as a Pareto improvement: many gain access, and no legal person loses a right that was ever warranted.

Rooted in the articulated public domain

The proposal develops Kop's earlier AI & Intellectual Property: Towards an Articulated Public Domain, published in the Texas Intellectual Property Law Journal in 2020, which argued for designing the public domain deliberately rather than treating it as the leftover of whatever rights fail to attach. The 2024 chapter applies that foundation to machine-generated subject matter under a named regime—so the two are best read as a sequence: the foundational article first, the autonomous-output application second. The same design-first instinct that animates Kop's responsible-innovation work, including the Ten Principles for Responsible Quantum Innovation, runs through the chapter: shape the rules before the defaults harden.

Why it matters for trans-Atlantic IP

Placing the argument inside a volume on trans-Atlantic harmonization is deliberate. The familiar questions—can an AI be an author, can an AI be an inventor—assume ownership is the only available category. Public Property from the Machine insists that public property is a category too—one the chapter argues is more defensible for output no human authored. For the United States and Europe, the practical question becomes not how to extend private rights to machines but what to agree to leave free. The fuller portrait of the scholar behind the proposal is set out in the Mauritz Kop profile. The chapter is a scholarly proposal rather than a statement of existing law—but it reframes a debate that has too often had only one answer on offer.

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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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Machine Learning & EU Data Sharing Practices

Stanford - Vienna Transatlantic Technology Law Forum, Transatlantic Antitrust and IPR Developments, Stanford University, Issue No. 1/2020

New multidisciplinary research article: ‘Machine Learning & EU Data Sharing Practices’.

Download the article here: Kop_Machine Learning and EU Data Sharing Practices-Stanford University

In short, the article connects the dots between intellectual property (IP) on data, data ownership and data protection (GDPR and FFD), in an easy to understand manner. It also provides AI and Data policy and regulatory recommendations to the EU legislature.

As we all know, machine learning & data science can help accelerate many aspects of the development of drugs, antibody prophylaxis, serology tests and vaccines.

Supervised machine learning needs annotated training datasets

Data sharing is a prerequisite for a successful Transatlantic AI ecosystem. Hand-labelled, annotated training datasets (corpora) are a sine qua non for supervised machine learning. But what about intellectual property (IP) and data protection?

Data that represent IP subject matter are protected by IP rights. Unlicensed (or uncleared) use of machine learning input data potentially results in an avalanche of copyright (reproduction right) and database right (extraction right) infringements. The article offers three solutions that address the input (training) data copyright clearance problem and create breathing room for AI developers.

The article contends that introducing an absolute data property right or a (neighbouring) data producer right for augmented machine learning training corpora or other classes of data is not opportune.

Legal reform and data-driven economy

In an era of exponential innovation, it is urgent and opportune that both the TSD, the CDSM and the DD shall be reformed by the EU Commission with the data-driven economy in mind.

Freedom of expression and information, public domain, competition law

Implementing a sui generis system of protection for AI-generated Creations & Inventions is -in most industrial sectors- not necessary since machines do not need incentives to create or invent. Where incentives are needed, IP alternatives exist. Autonomously generated non-personal data should fall into the public domain. The article argues that strengthening and articulation of competition law is more opportune than extending IP rights.

Data protection and privacy

More and more datasets consist of both personal and non-personal machine generated data. Both the General Data Protection Regulation (GDPR) and the Regulation on the free flow of non-personal data (FFD) apply to these ‘mixed datasets’.

Besides the legal dimensions, the article describes the technical dimensions of data in machine learning and federated learning.

Modalities of future AI-regulation

Society should actively shape technology for good. The alternative is that other societies, with different social norms and democratic standards, impose their values on us through the design of their technology. With built-in public values, including Privacy by Design that safeguards data protection, data security and data access rights, the federated learning model is consistent with Human-Centered AI and the European Trustworthy AI paradigm.

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Mauritz Kop becomes TTLF Fellow at Stanford University

AIRecht Partner joins Stanford Law School’s Transatlantic Thinktank

Honoured and thrilled to join Stanford Law School’s Transatlantic Thinktank and become TTLF Fellow at Stanford University. It is the Silicon Valley, California based Transatlantic Technology Law Forum’s objective to raise professional understanding and public awareness of transatlantic challenges in the field of law, science and technology, as well as to support policy-oriented research on transatlantic issues in the field.

Human Centred AI & IPR policy

My comparative, interdisciplinary research project focuses on Human Centred AI & IPR policy. How to realize an impactful transformative tech related IP (intellectual property) policy that facilitates an innovation optimum and protects our common Humanist moral values at the same time?

Focus beyond Intellectual Property Law

With an additional focus beyond IP, the research shall present ideas on how Europe and The United States could apply sustainable disruptive innovation policy pluralism (i.e. mix, match and layer IP alternatives such as competition law and government-market hybrids) to enable fair-trading conditions and balance the effects of exponential innovation within the Transatlantic markets. The research envisages that the presented ideas and viewpoints will be refined towards more actual policies in Brussels and Washington.

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Intellectual Property at Stanford Law School

USA IP Law at Stanford University

Stanford Law School has a long-standing tradition of sharing its expertise in Intellectual Property, Science and Technology law with legal professionals from around the world. In August 2019, AIRecht managing partner and strategic IP specialist Mauritz Kop had the pleasure to be part of a pre-selected international group of highly talented IP lawyers, counsels and scholars who had the opportunity to bring their professional skills to the next level and study complex IP issues related to Silicon Valley’s hi-tech industry, during an intensive international certificate summer program on U.S. IP law. The international professional American Intellectual Property Law Program at Stanford University is co-directed by Prof. Siegfried Fina, Prof. Mark Lemley and Dr. Roland Vogl.

SLS: A World’s Leading Law School at an Ivy Plus League University

Stanford University is an Ivy Plus League university. Ivy League schools such as Harvard, Yale, Princeton, MIT and Columbia University are viewed as the most prestigious, ranked among the best universities worldwide and have connotations of academic excellence. SLS is one of the world’s leading law schools. The Faculty draws international top talent to its magnificent campus. Stanford Law School’s Program in Law, Science & Technology (LST) has been ranked regularly among the top three intellectual property law programs in the United States, together with the IP Programs (LL.M. and J.D.) of the University of California-Berkeley and the University of New York.

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AI & Intellectual Property: Towards an Articulated Public Domain

New peer reviewed research article: ‘AI & Intellectual Property: Towards an Articulated Public Domain’ (download)

By Mauritz Kop

Link & citation at Texas Intellectual Property Law Journal (TIPLJ): 28 Tex. Intell. Prop. L. J. 297 (2020)

Link SSRN: https://ssrn.com/abstract=3409715

The article has been published in the Texas Intellectual Property Law Journal (2020, 28). TIPLJ is published in cooperation with the State Bar of Texas three times per year at the University of Texas School of Law. The Journal is the official journal of the State Bar of Texas Intellectual Property Law Section.

Res Publicae ex Machina (Public Property from the Machine)

Building upon the doctrinal body of knowledge, the article introduces a new public domain model for AI Creations and Inventions that crossed the autonomy threshold (i.e. no sufficient amount of human intervention that can be linked to the output): Res Publicae ex Machina (Public Property from the Machine). It includes examples.

Intellectual property framework AI systems

Besides that, the article describes the current legal framework regarding authorship and ownership of AI Creations, legal personhood, patents on AI Inventions, types of IP rights on the various components of the AI system itself (including Digital Twin technology), clearance of training data and data ownership.

Compact Artificial Intelligence & IP overview analysis

Main goal of this research is to offer an accessible, relatively compact Artificial Intelligence (AI) & IP overview analysis and in doing so, to provide some food for thought to interdisciplinary thinkers and policy makers in the IP, tech, privacy and freedom of information field.

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