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.
Sofia Ranchordás and Abram Klop
This chapter discusses the concept of data-driven regulation and governance in the context of smart cities by describing how these urban centres harness these technologies to collect and process information about citizens, traffic, urban planning or waste production. It describes how several smart cities throughout the world currently employ data science, big data, AI, Internet of Things (IoT), and predictive analytics to improve the efficiency of their services and decision-making. Furthermore, this chapter analyses the legal challenges of employing these technologies to influence or determine the content of local regulation and governance. This chapter explores in particular three specific challenges: the disconnect between traditional administrative law frameworks and data-driven regulation and governance; the effects of the privatization of public services and citizen needs due to the growing outsourcing of smart cities technologies to private companies; and the limited transparency and accountability that characterizes data-driven administrative processes. This chapter draws on a review of interdisciplinary literature on smart cities and offers illustrations of data-driven regulation and governance practices from different jurisdictions.
Cutting red tape, or unnecessarily burdensome rules, has become an important objective for policymakers around the world. While there is theoretical support for the notion that rules promulgated by government agencies, or agency rules, are a salient driver of red tape, there is very little empirical research that has tested this relationship. In this chapter, I put forward a research agenda that highlights the potential of data science techniques for studying the relationship between agency rules and red tape empirically. Techniques such as topic modelling and dictionary methods can be used to measure and test different elements of the agency rules – red tape relationship, including the sheer number of agency rules (or rule stock size), the magnitude of information requirements, as well as the red tape content of agency rules in terms of their overall costs and benefits.
This chapter looks at data science in the field of taxation, with an emphasis on the taxation of companies. The focus is on the point of view of companies as well as tax authorities. The former are affected by data science in relation to their financial accounts; and the quality thereof by the functioning of their internal auditing system. This then provides the input for financial accounts and tax accounts. The tax accounts a company maitains are discussed in this chapter as well as how data science – in particular in managing data – may relate to these tax accounts. Secondly, this chapter looks at the role of tax authorities and how the emerging field of data science impacts their work. Here too the wealth of information that reaches these authorities requires managing. The legal boundaries in which the authorities may make use of this data are discussed in this chapter as well. Finally, the contribution looks at, and provides comments on, the Intra-European Organization of Tax Administration’s 2017 report on data science and taxation, which discusses, among others, data management, predictive modelling, social network analysis and data visualization.
Helena Ursic, Ruslan Nurullaev, Míchel Olmedo Cuevas and Paweł Szulewski
Data has become an object for governments all around the world to pursue. Those countries that can access, use and control data will be the rulers not only of the digital realm, but also of the real world. Cross-border flows of information and unlimited access to data are the main facilitators of the emerging digital economy. How easily data can be obtained, how expensive it is, and to what legal rules it must adapt are all critical questions for everyone involved, not least for data scientists. This chapter provides an overview of the measures to restrict the use of data put in place by various countries by adopting various legislative measures with the common characteristic of encumbering cross-border data transfers. This chapter furthermore looks into the drivers of these localisation measures and how these measures impact data science. The authors propose a policy framework for a balanced approach to data localisation, which takes into account the needs for data science.
Methods of data research are becoming increasingly important in the legal domain. After explaining the concept of legal big data, to show that law is an area in which a lot of big data is available, this chapter discusses and illustrates several existing and potential applications of data research methods for lawyers and legal researchers. Particular opportunities exist with regard to: (1) predictions; (2) searching, structuring and selecting; and (3) decision-making and empirical legal research. These methods constitute an important contribution to legal practice and legal scholarship as they may provide novel and unexpected insights and considerably increase efficiency (less resources, more results) and effectiveness (more accurate and reliable results) of legal research, both in legal practice and legal scholarship. This may, among other things, result in improved legal services, new business models, new knowledge and a more solid basis for evidence-based policies and legislation. However, there are also several limits to, and drawbacks of, the use of these data research methods for law. From a methodological perspective, these include the lack of human intuition, an abundance of results that are not always relevant, limited insights in underlying causality, issues with repurposing, self-confirmation, self-fulfilling prophecies and reliability issues. It is concluded that, given the opportunities these developments provide for new business models for legal services and for legal research (both in legal practice and in legal scholarship), it is likely that these methods will be used on a larger scale in the near future and that new and additional methods will be developed. This will change to some extent the way legal work looks like and the job market for lawyers.