This book deals with one of the most important scientific developments of recent years, namely the exponential growth of data science. More than a savvy term that rings of robotics, artificial intelligence and other terms that for long were regarded as part of science-fiction, data science has started to become structurally embedded in scientific research. Data, meaning personal data as well as information in the form of digital files, has become available at such a large scale that it can lead to an expansion of knowledge through smart combinations and use of data facilitated by new technologies. This book examines the legal implications of this development. Do data-driven technologies require regulation, and vice versa, how does data science advance legal scholarship? Defining the relatively new field of data science requires a working definition of the term. By data science we mean the use of data (including data processing) for scientific research. The availability of massive amounts of data as well the relatively cheap availability of storage and processing power has provided scientists with new tools that allow research projects that until recently were extremely cumbersome if not downright impossible. These factors are also often described with the term ‘big data’, which is characterized by three Vs: volume, velocity and variety.The term data science is nonetheless broader, because it can also refer to the use of data sets that are large but still limited—and therefore, unlike big data, of a manageable size for processing.
This chapter aims to give an overview of the contractual issues that have arisen in relation to the use of data. Since the use of data has far-reaching consequences for consumer markets, the chapter focuses on issues that have arisen in those markets and the regulatory responses that have emerged, or are emerging, in consumer law. It considers in particular what effects the use of data has on the autonomy of contracting parties and on the balance of contractual fairness, and examines three more specific issues for consumer contract law, namely transparency, payment with data, and the question whether the ‘consumer’ concept needs adjusting. The focus of this chapter is mainly on the EU, with occasional references to the US, seeing that Europe has developed a fairly coherent regime of harmonised consumer contract law that in many aspects already applies to data-related contracts.
This chapter aims to give an overview of the contractual and non-contractual remedies related to loss of data. Different exoneration clauses in relation to data loss are looked at more closely for services such as Twitter, Google, Facebook. The chapter furthermore discusses not only the loss of non-personal data, but also touches upon the problems of the case of loss of personal data. Several topics are discussed further, including the difficulties with affixing liability when it is unclear who ‘owns’ the data and what the value of the lost data is (i.e., how to quantify the damages).
Eric Tjong Tjin Tai
Liability for robots and algorithms is at present unclear. A comparative overview shows that while several grounds for liability may apply, depending on the jurisdiction, there are still significant gaps, in particular regarding liability for algorithms. Several changes would be required to provide effective protection of interests that may be harmed by defective autonomous systems. By careful regulation a proper balance may be obtained between allowing innovation without undue harm for society.
This chapter aims to give an overview of the issues related to consumer contracts that have arisen due to the rapid emergence of the Internet of Things (IoT). It indicates the challenges for the traditional concepts and rules of private law posed by the development of ‘smart’ devices. It analyses in particular the consequences of personalisation of Internet-enabled devices, the dependency of the user on continuous provision of services by the manufacturer or a third party, the problem of conformity of a ‘smart’ device with the contract, the issues related to replenishment services as well as the relevance of traditional notions of sale and ownership in the context of the IoT. The chapter examines the question of data as counter-performance in contracts concerning ‘smart’ devices. Furthermore, it investigates the potential of the IoT to increase the risk of digital market manipulation and the corresponding regulatory responses.
This chapter delves into the issue of the legal qualification of data within property law, which gives rise to a remarkable paradox. On the one hand, it is a fact that a rapidly growing number of companies have discovered the (potential) economic value of data and have come to consider, use and treat them as regular business assets. As a result, data are gathered, processed, analyzed and also ‘sold’ on a large scale and on a daily basis. On the other hand, the author shows that – taking the example of Belgian law – that property law denies the very existence of data files. They are not susceptible to a right of pledge or attachment. The chapter looks at the indirect way in which it is possible to pledge and attach data files by way of the data carrier, the sui generis database right, and also discusses alternatives. Moreover, the chapter also looks at whether pledging of and foreclosure on data files can be considered a justified processing of personal data in light of the GDPR.
This chapter examines the somewhat jumbled relationship between data and intellectual property law, with a special focus on copyrights, patents, and trade secrets. Although these bodies of law are deeply concerned with and influenced by new technologies, they offer limited protections to the new industries forming around data today. Traditional copyright protection for data and databases is relatively thin, and the patentability of algorithms that can process data is somewhat unpredictable under current American jurisprudence. Meanwhile, although data may be the subject of trade secret protection, liability under this body of law extends only to those who wrongfully use or disclose valuable secret data. Responding in part to the limitations of traditional IP law, European policymakers in the 1990s enacted a special form of sui generis rights for databases and continue to explore useful new policies today. Despite repeated efforts by US lawmakers, no similar protections have been enacted into American law. In addition to exploring how the law applies to data, this chapter briefly highlights how new industrial and commercial uses of data connect with the policies underlying IP law—most significantly, the twin goals of promoting innovation and disclosing technological information.
Anne Lafarre and Christoph Van der Elst
This chapter looks at how legal tech can offer smart solutions for classical corporate governance inefficiencies, like the agency problem and the old-fashioned Annual General Meeting of Shareholders (hereinafter: the AGM). This chapter focusses on the smart solutions of legal tech, thereby investigating and critically assessing its benefits and risks in the field of corporate governance and the AGM. The chapter provides a general introduction to the agency problem and the associated agency costs between shareholders and their corporate board members in corporate governance and introduces blockchain technology as a solution to the agency problem, thereby discussing the decentralized autonomous organization (hereinafter: The DAO). Although blockchain offers the possibility to create a decentralized peer-to-peer network, we will see that The DAO had still some governance problems. Therefore, the authors consider blockchain and smart contracting technology to decrease the monitoring and bonding costs of companies, by introducing and evaluating a blockchain based AGM.
Rupprecht Podszun and Stephan Kreifels
With data as an important parameter for success in markets, issues of the data economy become relevant for competition law. This field of the law traditionally deals with the functioning of market mechanisms and power of individual firms. Competition authorities and courts have to adapt to paradigm shifts in many areas: the value of data can hardly be monetized. Markets in the digital economy are often multi-sided and shaped by strong network effects. Powerful platform operators may control access for customers. Access to data may become a market-entry barrier. Finally, the use of data to feed algorithms and AI may even change competition as such. We examine the data economy from a competition law perspective and present the relevant theories of harm by means of a sample of leading cases, mainly of European courts and competition authorities. Finally, we touch upon first regulatory responses and related questions.
Maria Grazia Porcedda and David S. Wall
This chapter explores the relationship between data science, data crimes and the law. It illustrates how Big Data is responsible for Big Data crimes, but that data science and law could mutually help each other by identifying the ethical and legal devices necessary to enable Big Data analytic techniques to identify the key stages at which data crimes take place and also prevent them. This chapter will therefore explore the use of data science (Big Data) analytics for the fight against (cyber) crime and identify the implications for the law, and possible solutions. In particular, it discusses the literature on Big Data and Crime that considers the development of predictive models of crime that can be used to assist criminal justice professionals, such as police management, to allocate resources more efficiently. The authors contribute to this debate in three ways. The first contribution is theoretical and stems from a dialogue with data ethicists, as the authors propose that it is crucial to account for the endogenous and exogenous limitations of data science. Secondly, they demonstrate how Big Data itself has created new criminal markets for Big Data which encourage data crime. The development of which creates new challenges for law enforcement agencies on an unprecedented scale. The third contribution is that the much-hyped and much critiqued Big Data analytic techniques could actually be applied, in certain circumstances and subject to appropriate rules of engagement which take into account the nature of the data, to an analysis of data crime in order to help investigators understand it more thoroughly and possibly even detect the point of crimes to assist in the tracking of offenders.