ABSTRACT

Human mood enhancement technology offers promising benefits for their users ‘ health and well-being while raising critical legal issues that must be addressed. This chapter, therefore, analyses the applicable human rights legal frameworks at the UN, CoE and EU levels concerning this type of technology.

Chapter 3 focuses on the principles and rights applicable to mood HETs stemming from the International Covenant on Civil and Political Rights (hereinaft er: ICCPR), the International Covenant on Economic, Social and Cultural Rights (hereinaft er: ICESC), the Universal Declaration of Bioethics and Human Rights (hereinaft er: UDBHR), the Convention on Human Rights and Biomedicine (hereinaft er: the Oviedo Convention), the Framework Convention on artificial intelligence, human rights, democracy and the rule of law (hereinaft er: the AI Convention), the European Convention on Human Rights (hereinaft er: ECHR), the Modernised Convention for the Protection of Individuals with Regard to the Processing of Personal Data (hereinaft er: the Convention 108 + ), the European Social Charter (hereinaft er: ESC), the Charter of Fundamental Rights of the European Union (hereinaft er: CFREU), including the secondary EU legislation whose applicability is established on the example of a case study technology outlined in the Introduction. This analysis also includes the examination of the jurisprudence of the European Court of Human Rights (hereinaft er: ECtHR) and the Court of Justice of the European Union (hereinaft er: CJEU).

EU data legislation and the GDPR do respectively not engage in the same manner with data commodification, which notably manifests in different ways in which they respectively seek to empower individuals by granting them ‘data control‘. The misalignment is not a problem as such but evidences the fact that the enhancement of data control of individuals, by both EU data legislation and the GDPR, cannot constitute the bridge the two. In other words, the compatibility model of EU data legislation between data markets and data protection is based on what turns out to be a myth of congruence. Part IV of this book is dedicated to the analysis of the implications of the misalignment between EU data legislation and the GDPR concerning data commodification, for the compatibility model of EU data legislation between data markets and data protection. I focus on two sets of problems. In Chapter 7, I discussed the heightened likelihood that data subjects qualify as data controllers within the meaning of the GDPR, as a result of EU data legislation. I found that the decentralized data processing environment that EU data legislation establishes as well as the type of data control that EU data legislation puts forward and the tools and mechanisms for enhancing it, do indeed heighten the risk that data subjects act and qualify as data controllers.

ETHICS VERSUS LAW OR ETHICS AND LAW ?

INTRODUCTORY REMARKS

572. Chapter 2 mapped and analysed arguments oft en raised in the ethics debate concerning human (mood) enhancement technologies. As outlined there, some of them cannot be meaningfully used in the form in which they are commonly presented for the legislative debate concerning these technologies based on the principle of neutrality of law. These are mostly the arguments with strong religious and ideological inklings (such as unnaturalness, playing god, cheating and similar arguments), as well as arguments with the problem of rhetorical types, whereas the object of criticism they are trying to address is left ambiguous, vague, imprecise, or simply unclear (these are considered to be the meta-arguments or arguments with methodological issues).

BUILDING INSTRUCTIONS TOWARD A DATA COMMODIFICATION SPECTRUM

The present chapter conceptualizes data commodification as a spectrum, following the approach of Radin (as explained in the previous chapter) except where deviations are made necessary because of the local specificities of data. This chapter builds a spectrum, ranging from complete data commodification on the one hand to complete data non-commodification on the other hand. On the spectrum, I systematically identify and cluster the various schools of thoughts that engage with the data commodification phenomenon depending on the degree to which they aim for data commodification, why and how. Then, I will use this spectrum to situate and cluster how EU data legislation and the GDPR respectively engage with data commodification. To do that, and just like Radin, I need two main things. First, I need thematerials, namely, and just like Radin, the relevant literature that reports on the data commodification phenomenon. Second, I need commodification indicia that are well-suited for data commodification.

The relevant literature can be found with data governance, a rich literature that includes various disciplines including economics, philosophy, law, sociology and others, more or less structured around schools of thoughts. Data governance can be defined, broadly, as the system of rights and responsibilities that determine who can take what actions with respect to data, including how such rights and responsibilities interact one with the others.

FOCUS ON CONNECTED PRODUCT DATA

At the time of writing, the Data Act is not yet in force. Hotly debated during its legislative adoption, the Data Act seeks to walk a ridgeline. On the one hand, its goal is to unleash the potential of data held by private actors to fulfil their alleged potential in being used and reused for various purposes, thus requiring data sharing. On the other hand, the Data Act recognizes that data sharing may have to be qualified for a number of reasons including the preservation of incentives for private actors to keep investing – which takes the legal form of the utmost care for their trade secrets – and personal data protection. To walk this ridgeline, the Data Act proceeds with surgical touches. While the Open Data Directive is based on the principle that data held by public sector bodies should be made available to the general public for further reuse, the Data Act regulates more or less extensively a few – non-exhaustive – situations in which data sharing is required, accompanied with conditions.

In this chapter, I focus on one such situation: Connected product data, namely data generated by the use of products such as smart wearables, smart farming machinery, connected cars, etc. Connected product data constitute a prime example of the attempt of the EU legislature to lay down provisions that look and taste like property law for data while accounting for what are considered to be their specificities. Connected product data have long been a key concern of the EU data policy and especially the key focus on the ‘data producer‘ s right ‘ option once contemplated by the Commission (and then abandoned).

In Part I of the book, I designed a data commodification spectrum, based on dataspecific commodification indicia. In Parts II and III respectively, I used the spectrum to identify how the Data Act and the DGA (together, EU data legislation) and the GDPR engage with data commodification or, in other words, to what extent and how they commodify data. On the one hand, I found that both the Data Act and the DGA display a high level of consistency between them. I clustered them both (EU data legislation) between the efficient and the fair data market paradigms, and especially fair data distribution under the latter paradigm. In the parlance of Radin, EU data legislation pursues a ‘negative liberal approach‘ in the sense that data markets constitute the only principle. Limitations to data commodification are laid down but within market framework and values and without a positive approach, which, according to Radin, offers a slippery road to complete data commodification.

On the other hand, I clustered the GDPR on the data commodification spectrum, with a focus on data control, which EU data legislation features as a bridge between data markets and data protection or, in other words, for the compatibility model of EU data legislation.

PROBLEM STATEMENT AND AMBITION

1. Political institutions on both sides of the Atlantic commissioned extensive reports to predict what emerging and future technologies with the potential to improve or change human capacities might look like. These reports urge the development of guidelines and recommendations for policies, regulation and governance of human enhancement technologies (hereinaft er: HETs) in a socially desirable way. Furthermore, academics, including legal scholars and ethicists, amongst others, discussed HETs from their respective specific angles. However, these analyses oft en lack a broader exploration and an overall perspective on the ethical and legal issues associated with a particular type of human enhancement technology.

To conceptualize data commodification – as per the following chapter – I first need to be able to rely on an appropriate foundational framework. The identification of it constitutes the object of the present Chapter 1. I first introduce the key notions connected to markets and commodification as a process leading ultimately to markets as well as commodification studies as the strand of the literature that analyzes these processes. Then, I introduce the seminal conceptualization of commodification by Radin, which constitutes the foundational framework for this book. At this point, it remains to be further explained and justified why it is relevant for my present endeavor. The justification is threefold and pertains, in turn, to the following elements: The justification of commodification studies as a field; the comparability of data with sex, body parts and babies in that they are also ‘contested commodities‘, which calls to mind Radin‘s spectrum approach to commodification; and finally, the justification of the specific relevance of Radin‘s approach amidst commodification studies, given the existence of other spectrum approaches to the commodification phenomenon.

Part I was dedicated to the understanding of the data commodification phenomenon. Based on the seminal work of Radin on commodification, I designed a data commodification spectrum, ranging from data complete commodification to data complete non-commodification. To do that, I adapted the ‘commodification indicia‘ to the specificities of data commodification, leading to four ‘data-commodification indicia‘. They pertain to the identification of the commodity – and thus the relevant resource and object at stake, the identification of the relevant actors, of the relevant framework and, finally, of the value(s) at stake. To form the data commodification spectrum, I clustered data governance normative arguments where I could identify a significant portion of the literature as falling into the ‘efficient data market paradigm‘ while the ‘fair data market paradigm‘ is also a prominent portion of the debate – with internal variations. Other than that, I could find no consistent and well-developed paradigm. I analyzed the data commons literature, yet a rather immature and scattered one but full of teachings. An element learned from the data commons literature that bears significance for the present book is the conscious deliberation over what should be considered as the relevant resource that contrasts with the focus on ‘data‘ (and data only) in both the efficient and the fair data markets paradigms. Finally, I identified a burgeoning cluster that I referred to as ‘societal data governance‘ that essentially draws data governance conclusions from the characterization of data commodification as a key and problematic element of data capitalism.

INTRODUCTION

46. People have been trying to become smarter, advance their skills and knowledge, or improve themselves in many different ways, respects, and degrees. All of these improvements could come in different shapes and formats. One may think of a person following a healthy diet to improve their health, someone reading a book to advance their knowledge on a given topic, or running every evening to prepare their body strength to finish a marathon.

47. More recently, people have also been striving to improve themselves using different technological tools. For example, new and emerging technologies are used to improve sports performances, concentration at work, overall health, and similar. The term ‘ technologies ‘ should mean the application of scientific knowledge to the practical aims of human life.

WHAT THIS BOOK DOES NOT CONTAIN AND WHAT IT DOES CONTAIN

Beyond online platforms, data are nowadays used as key economic resources in a broad array of contexts. Huge amounts of data are, for example, generated in sectors as disparate as agriculture, home appliances and wearables. In the agriculture sector, data collected in farms can inform on the quality of soils and thus on the need for intrants or to ‘monitor crop development‘. In the home appliances sector, data generated throughout the use of ‘smart‘ devices may for example reduce electricity consumption and support individuals in their cooking experience. Data generated through the use of wearable fitness trackers can provide information, for example, on the sleeping patterns of individuals wearing them, which could be used for medical purposes.

That data are valuable economic resources has become so self-evident that every book on data worthy of the name is expected to start with a discussion on this phenomenon together with a metaphor that, according to the author, best describes data as economic resources. Are data ‘the new oil‘insofar as they are turned into the infrastructure for today‘s economy and society? Or are they squarely‘the lifeblood‘ of society? Or, alternatively, should data be compared as air, speaking to their volatility? Or maybe data are best described as rivers, that concern all the persons through the property of whom they flow? Or yet another flourishing metaphor views data as labor.