Regulating Transformative Technology in The Quantum Age: Intellectual Property, Standardization & Sustainable Innovation

Stanford - Vienna Transatlantic Technology Law Forum, Transatlantic Antitrust and IPR Developments, Stanford University, Issue No. 2/2020 (Forthcoming)

New Stanford cutting edge tech law research: “Regulating Transformative Technology in The Quantum Age: Intellectual Property, Standardization & Sustainable Innovation”.

Quantum technology has many legal aspects

The behavior of nature at the smallest scale can be strange and counterintuitive. In addition to unique physical characteristics, quantum technology has many legal aspects. In this article, we first explain what quantum technology entails. Next, we discuss implementation and areas of application, including quantum computing, quantum sensing and the quantum internet. Through an interdisciplinary lens, we then focus on intellectual property (IP), standardization, ethical, legal & social aspects (ELSA) as well as horizontal & industry-specific regulation of this transformative technology.

The Quantum Age raises many legal questions

The Quantum Age raises many legal questions. For example, which existing legislation applies to quantum technology? What types of 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? Should we create global quantum safety, security and interoperability standards and make them mandatory in each area of application? In what way can quantum technology enhance artificial intelligence (AI) that is legal, ethical and technically robust?

Regulating quantum computing, quantum sensing & the quantum internet

How can policy makers realize these objectives and regulate quantum computing, quantum sensing and the quantum internet in a socially responsible manner? Regulation that addresses risks in a proportional manner, whilst optimizing the benefits of this cutting edge technology? Without hindering sustainable innovation, including the apportionment of rights, responsibilities and duties of care? What are the effects of standardization and certification on innovation, intellectual property, competition and market-entrance of quantum-startups?

The article explores possible answers to these tantalizing questions.

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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.