Quantum Computing and Intellectual Property Law

Berkeley Technology Law Journal, Vol. 35, No. 3, 2021, Forthcoming

New Stanford University Beyond IP Innovation Law research article: “Quantum Computing and Intellectual Property Law”.

By Mauritz Kop

Citation: Kop, Mauritz, Quantum Computing and Intellectual Property Law (April 8, 2021). Berkeley Technology Law Journal 2021, Vol. 35, No. 3, pp 101-115, February 8, 2022, https://btlj.org/2022/02/quantum-computing-and-intellectual-property-law/

Please find a short abstract below:

Intellectual property (IP) rights & the Quantum Computer

What types of intellectual property (IP) rights can be vested in the components of a scalable quantum computer? Are there sufficient market-set innovation incentives for the development and dissemination of quantum software and hardware structures? Or is there a need for open source ecosystems, enrichment of the public domain and even democratization of quantum technology? The article explores possible answers to these tantalizing questions.

IP overprotection leads to exclusive exploitation rights for first movers

The article demonstrates that strategically using a mixture of IP rights to maximize the value of the IP portfolio of the quantum computer’s owner, potentially leads to IP protection in perpetuity. Overlapping IP protection regimes can result in unlimited duration of global exclusive exploitation rights for first movers, being a handful of universities and large corporations. The ensuing IP overprotection in the field of quantum computing leads to an unwanted concentration of market power. Overprotection of information causes market barriers and hinders both healthy competition and industry-specific innovation. In this particular case it slows down progress in an important application area of quantum technology, namely quantum computing.

Fair competition and antitrust laws for quantum technology

In general, our current IP framework is not written with quantum technology in mind. IP should be an exception -limited in time and scope- to the rule that information goods can be used for the common good without restraint. IP law cannot incentivize creation, prevent market failure, fix winner-takes-all effects, eliminate free riding and prohibit predatory market behavior at the same time. To encourage fair competition and correct market skewness, antitrust law is the instrument of choice.

Towards an innovation architecture that mixes freedom and control

The article proposes a solution tailored to the exponential pace of innovation in The Quantum Age, by introducing shorter IP protection durations of 3 to 10 years for Quantum and AI infused creations and inventions. These shorter terms could be made applicable to both the software and the hardware side of things. Clarity about the recommended limited durations of exclusive rights -in combination with compulsory licenses or fixed prized statutory licenses- encourages legal certainty, knowledge dissemination and follow on innovation within the quantum domain. In this light, policy makers should build an innovation architecture that mixes freedom (e.g. access, public domain) and control (e.g. incentive & reward mechanisms).

Creating a thriving global quantum ecosystem

The article concludes that anticipating spectacular advancements in quantum technology, the time is now ripe for governments, research institutions and the markets to prepare regulatory and IP strategies that strike the right balance between safeguarding our fundamental rights & freedoms, our democratic norms & standards, and pursued policy goals that include rapid technology transfer, the free flow of information and the creation of a thriving global quantum ecosystem, whilst encouraging healthy competition and incentivizing sustainable innovation.

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Democratic Countries Should Form a Strategic Tech Alliance

Stanford - Vienna Transatlantic Technology Law Forum, Transatlantic Antitrust and IPR Developments, Stanford University, Issue No. 1/2021

New Stanford innovation policy research: “Democratic Countries Should Form a Strategic Tech Alliance”.

Exporting values into society through technology

China’s relentless advance in Artificial Intelligence (AI) and quantum computing has engendered a significant amount of anxiety about the future of America’s technological supremacy. The resulting debate centres around the impact of China’s digital rise on the economy, security, employment and the profitability of American companies. Absent in these predominantly economic disquiets is what should be a deeper, existential concern: What are the effects of authoritarian regimes exporting their values into our society through their technology? This essay will address this question by examining how democratic countries can, or should respond, and what you can do about it to influence the outcome.

Towards a global responsible technology governance framework

The essay argues that democratic countries should form a global, broadly scoped Strategic Tech Alliance, built on mutual economic interests and common moral, social and legal norms, technological interoperability standards, legal principles and constitutional values. An Alliance committed to safeguarding democratic norms, as enshrined in the Universal Declaration of Human Rights (UDHR) and the International Covenant on Civil and Political Rights (ICCPR). The US, the EU and its democratic allies should join forces with countries that share our digital DNA, institute fair reciprocal trading conditions, and establish a global responsible technology governance framework that actively pursues democratic freedoms, human rights and the rule of law.

Two dominant tech blocks with incompatible political systems

Currently, two dominant tech blocks exist that have incompatible political systems: the US and China. The competition for AI and quantum ascendancy is a battle between ideologies: liberal democracy mixed with free market capitalism versus authoritarianism blended with surveillance capitalism. Europe stands in the middle, championing a legal-ethical approach to tech governance.

Democratic, value-based Strategic Tech Alliance

The essay discusses political feasibility of cooperation along transatlantic lines, and examines arguments against the formation of a democratic, value-based Strategic Tech Alliance that will set global technology standards. Then, it weighs the described advantages of the establishment of an Alliance that aims to win the race for democratic technological supremacy against disadvantages, unintended consequences and the harms of doing nothing.

Democracy versus authoritarianism: sociocritical perspectives

Further, the essay attempts to approach the identified challenges in light of the ‘democracy versus authoritarianism’ discussion from other, sociocritical perspectives, and inquires whether we are democratic enough ourselves.

How Fourth Industrial Revolution (4IR) technology is shaping our lives

The essay maintains that technology is shaping our everyday lives, and that the way in which we design and utilize our technology is influencing nearly every aspect of the society we live in. Technology is never neutral. The essay describes that regulating emerging technology is an unending endeavour that follows the lifespan of the technology and its implementation. In addition, it debates how democratic countries should construct regulatory solutions that are tailored to the exponential pace of sustainable innovation in the Fourth Industrial Revolution (4IR).

Preventing authoritarianism from gaining ground

The essay concludes that to prevent authoritarianism from gaining ground, governments should do three things: (1) inaugurate a Strategic Tech Alliance, (2) set worldwide core rules, interoperability & conformity standards for key 4IR technologies such as AI, quantum and Virtual Reality (VR), and (3) actively embed our common democratic norms, principles and values into the architecture and infrastructure of our technology.

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We hebben dringend een recht op dataprocessing nodig

Deze column is gepubliceerd op platform VerderDenken.nl van het Centrum voor Postacademisch Juridisch Onderwijs (CPO) van de Radboud Universiteit Nijmegen. https://www.ru.nl/cpo/verderdenken/columns/we-dringend-recht-dataprocessing-nodig/

Bij een datagedreven economie hoort een gezond ecosysteem voor machine learning en artificial intelligence. Mauritz Kop beschrijft de juridische problemen en oplossingen hierbij. “We hebben dringend een recht op dataprocessing nodig.”

5 juridische obstakels voor een succesvol AI-ecosysteem

Eerder schreef ik dat vraagstukken over het (intellectueel) eigendom van data, databescherming en privacy een belemmering vormen voor het (her)gebruiken en delen van hoge kwaliteit data tussen burgers, bedrijven, onderzoeksinstellingen en de overheid. Er bestaat in Europa nog geen goed functionerend juridisch-technisch systeem dat rechtszekerheid en een gunstig investeringsklimaat biedt en bovenal is gemaakt met de datagedreven economie in het achterhoofd. We hebben hier te maken met een complex probleem dat in de weg staat aan exponentiële innovatie.

Auteursrechten, Privacy en Rechtsonzekerheid over eigendom van data

De eerste juridische horde bij datadelen is auteursrechtelijk van aard. Ten tweede kunnen er (sui generis) databankenrechten van derden rusten op (delen van) de training-, testing- of validatiedataset. Ten derde zullen bedrijven na een strategische afweging kiezen voor geheimhouding, en niet voor het patenteren van hun technische vondst. Het vierde probleempunt is rechtsonzekerheid over juridisch eigendom van data. Een vijfde belemmering is de vrees voor de Algemene verordening gegevensbescherming (AVG). Onwetendheid en rechtsonzekerheid resulteert hier in risicomijdend gedrag. Het leidt niet tot spectaculaire Europese unicorns die de concurrentie aankunnen met Amerika en China.

Wat is machine learning eigenlijk?

Vertrouwdheid met technische aspecten van data in machine learning geeft juristen, datawetenschappers en beleidsmakers de mogelijkheid om effectiever te communiceren over toekomstige regelgeving voor AI en het delen van data.

Machine learning en datadelen zijn van elementair belang voor de geboorte en de evolutie van AI. En daarmee voor het behoud van onze democratische waarden, welvaart en welzijn. Een machine learning-systeem wordt niet geprogrammeerd, maar getraind. Tijdens het leerproces ontvangt een computer uitgerust met kustmatige intelligentie zowel invoergegevens (trainingdata), als de verwachte, bij deze inputdata behorende antwoorden. Het AI-systeem moet zelf de bijpassende regels en wetmatigheden formuleren met een kunstmatig brein. Algoritmische, voorspellende modellen kunnen vervolgens worden toegepast op nieuwe datasets om nieuwe, correcte antwoorden te produceren.

Dringend nodig: het recht op dataprocessing

De Europese Commissie heeft de ambitie om datasoevereiniteit terug te winnen. Europa moet een internationale datahub worden. Dit vereist een modern juridisch raamwerk in de vorm van de Europese Data Act, die in de loop van 2021 wordt verwacht. Het is naar mijn idee cruciaal dat de Data Act een expliciet recht op dataprocessing bevat.

Technologie is niet neutraal

Tegelijkertijd kan de architectuur van digitale systemen de sociaal-maatschappelijke impact van digitale transformatie reguleren. Een digitaal inclusieve samenleving moet technologie actief vormgeven. Technologie an sich is namelijk nooit neutraal. Maatschappelijke waarden zoals transparantie, vertrouwen, rechtvaardigheid, controle en cybersecurity moeten worden ingebouwd in het design van AI-systemen en de benodigde trainingdatasets, vanaf de eerste regel code.

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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) Online Digest 2020, Forthcoming

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

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.

Data has become a primary resource

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.

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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’.

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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