In order to evaluate the compatibility model of EU data legislation between data markets and data protection, I need a unique relevant yardstick, namely a conceptualization of data commodification. While data commodification has been referred to in the literature, there is surprisingly no conceptualization of this phenomenon. Data commodification is oft en pictured as a monolithic phenomenon whereby data would be either ‘commodified‘ or not. Authors engaging with this phenomenon provide little explanation as to which criterion(a) would trigger a qualification as either ‘commodified‘ or not. Because used mainly by fierce opponents to this phenomenon, the term ‘commodification‘ mainly serves to cast shame on certain data governance arrangements. What is missing, for the purpose of the present endeavor, is a conceptualization of data commodification that reports on three main aspects: First, the reasons why data commodification – and ultimately data markets – is a disputed phenomenon, or in other words why data markets and/or commodification is oft en met with discomfort – if not squarely discontent. Second, how the law relates to data commodification and, third, how data commodification relates to values that are not necessarily exchange or market ones such as data protection. The conceptualization of the data commodification phenomenon in a way that report on these specifications constitutes the object of Part I of this book.

ABSTRACT

Chapter 2 maps and analyses the oft en-occurring arguments in the ethics debate concerning human (mood) enhancement technologies. This debate concerns arguments with strong religious and ideological inklings (such as the unnaturalness concern, the playing god argument, medicalisation, the cheating argument, and the selfishness argument). These arguments sometimes have problems of a rhetorical nature, leaving the aim of their objection unclear. The rhetorical problems are not as such arguments but dimensions of ideological, religious and ethical issues, in that they sometimes are mixed and do not clarify whether they concern individuals or species.

Moreover, this debate also involves the arguments dealing with issues of justice, identity, autonomy, dignity, privacy, safety and prevention of harm in the context of human (mood) enhancement technologies. Following their analysis and evaluation, a dedicated section provides an integrated overview of how these arguments connect and overlap.

Mapping and analysis of the oft en-occurring arguments in the ethics debate concerning human (mood) enhancement technologies based on which normative recommendations are formulated for lawmaking and policymaking purposes are performed by applying the wide refl ective equilibrium method developed by John Rawls. The method consists of three levels of considerations that must be distinguished: descriptive and normative theories, moral principles and considered moral judgements. It will be assumed that the equilibrium exists when the three levels cohere and are mutually supportive.

In the last years, data have been increasingly considered as resources, in the midst of various and possibly contradictory values, objectives and interests. For example, data from smart farming equipment can be used to gain insights as to the properties of the farm soils and thus help tailor the required intrants. Data from wearable fitness trackers can provide information on the health of individuals wearing them and thus help tailor medical treatment. Data are also expected to feed the development of AI, for example by training algorithms in various scenarios. Until recently, data were not or little regulated as such. Now, the EU legislature attempts to incorporate the resource function of data in law. Once toyed with, the option to establish data ownership – in the form of a ‘data producer’s right’– was soon abandoned. Following the European Data Strategy of the Commission, the EU has embarked on the path of a data-specific (sui generis) and data-focused set of legislations. The objective of the EU therein is to add to data protection, rules that account for the function of data as a resource with the ambition to establish and support data markets. The Data Act and the DGA – the focus of this book – are thus expected by the EU legislature to constitute the private law for data – or even a ‘data law’. Such EU data legislation will then be complemented with ‘public law’initiatives as part of data spaces regulations.

DATA SUBJECTS AS DATA CONTROLLERS CONCERNING THE PROCESSING OF DATA RELATING TO THEMSELVES?

In order to allocate the responsibility to comply with legal requirements, the GDPR relies mainly on the notion of ‘(data) controller‘, defined as

The data controller is the main person responsible for compliance with the GDPR. 1165 Where several controllers ‘jointly determine the purposes and means of processing‘, they are joint controllers and shall arrange their respective responsibilities for compliance between themselves, in a transparent manner and in a way that ‘duly reflects the respective roles and relationships [that they have] vis-à-vis the data subjects‘. Such an arrangement cannot be enforced against data subjects.

The GDPR regulates other roles, and in particular this of the ‘processor‘, defined as the person who processes personal data on behalf of the controller. 1167 Should the processor process data beyond the instructions given by the data controller, they would then qualify as a controller.