The Right to Process Data for Machine Learning Purposes in the EU
18 Pages Posted: 26 Aug 2020 Last revised: 4 Apr 2021
Date Written: June 22, 2020
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. Normative preferences about how related technology laws ought to be designed should define sustainable exponential innovation policy. These preferences are dynamic and contextual.
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.
To make AI and machine learning thrive, we should critically re-examine the applicability and scope of intellectual property rights to data, including copyrights, sui generis database rights and trade secrets. The article demonstrates that exclusive de facto possession or control over machine learning input training, testing and validation datasets hinders healthy competition, a fair level playing field and rapid European innovation. The article rejects exclusive legal ownership rights over autonomously machine generated non-personal data, including AI made creations and inventions: this output belongs to the public domain. Machines do not need incentives, people need freedom of expression and businesses need freedom to operate.
Synchronous to harmonized legislation, the social impact of digital transformation can be balanced and regulated by the architecture of digital systems. Embedding values in design should become a fundamental starting point of our data paradigm.
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, drawing inspiration from the works of canonical thinkers such as Locke, Marx, Kant and Hegel.
Whether or not data as digital assets are ultimately admitted to the numerus clausus of legal objects i.e. acknowledged as subject matter eligible for private ownership, or whether other modalities and states of property are being developed, the article maintains that there should also 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.
The article 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.
Keywords: Right to Process Data, Data Sharing Access and Re-Use, Ius Utendi (Usus) Et Fruendi (Fructus), Quasi Usufruct, Locke, Kant, Machine Learning, AI, Intellectual Property, European Data Act, Training Datasets, Database Rights, Copyright, Trade Secrets
JEL Classification: O24, O31, O32, O33, O34, O35, O38, O39, K11, K12, K39, F13, Z18
Suggested Citation: Suggested Citation