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The main objective of this paper is the evidence of the burning issue concerning "managers" and, in particular, their participation in labor unions and employee Councils. To achieve this goal, the relevant contemplation initiates the elucidation of the basic In addition, one could easily understand the significance of such contemplation, because most of the arguments are raised from the clarification of such notions as 'manager', 'syndicate' and 'employee's Council' The article proceeds by making special reference to the existing legislative initiatives with the following interpretative attempts of the relative articles: Finally, there is a brief overview of the latest developments in German theory and jurisprudence.

The paper dissects the intricacies of automated decision making (ADM) and urges for refining the current legal definition of artificial intelligence (AI) when pinpointing the role of algorithms in the advent of ubiquitous computing, data analytics and deep learning. Whilst coming up with a toolkit to measure algorithmic determination in automated/semi-automated tasks might be proven to be a tedious task for the legislator, our main aim here is to explain how a thorough understanding of the layers of ADM could be a first good step towards this direction: AI operates on a formula based on several degrees of automation employed in the interaction between the programmer, the user, and the algorithm. The paper offers a fresh look at AI, which exposes certain vulnerabilities in its current legal interpretation. To highlight this argument, analysis proceeds in two parts: Part 1 strives to provide a taxonomy of the various levels of automation that reflects distinct degrees of human–machine interaction. Part 2 further discusses the intricate nature of AI algorithms and considers how one can utilize observed patterns in acquired data. Finally, the paper explores the legal challenges that result from user empowerment and the requirement for data transparency.

Big Data is a relatively recent phenomenon, but has already shown its potential to drastically alter the relationships between businesses, individuals, and governments. The issues surrounding privacy of the online users (Mayer-Shoenberger, Cukier 2013) and the overall ethical challenges involved (Schroeder, 2014) make big data a topical issue, especially in the aftermath of the Snowden revelations. Many organisations now control vast amounts of raw data, and those industry players with the resources to mine that data to create new information have a significant advantage in the big data market. The use of predictive analytics in processing information tracked across different platforms to identify trends in the behaviour of individuals further adds value to big data (Fotopoulou, 2014) and makes it an important asset for any commercial entity. This rapid commodification of personal data has given rise to a new approach with regard its legal protection in the era of big data: a shift from the traditional privacy protection regime to a wider protection under property law is considered by scholars as an appropriate legal response to the phenomenon of monetisation of personal data, once seen through the lens of big data (Victor, 2013). The aim of this chapter is to identify the legal grounds for the ownership of big data: who legally owns the petabytes and exabytes of information created daily? Does this belong to the users, the data analysts or to the data brokers and various infomediaries? The chapter presents a succinct overview of the legal ownership of big data by examining the key players in control of the information at each stage of the processing of big data. It then moves on to describe the current legislative framework with regard to data protection and concludes in additional techno-legal solutions offered to complement the law of big data in this respect, with a particular focus on the European context .

The rise of wearable tech, namely devices with sensors measuring the user's daily activities and habits seems to be suggesting a paradox in the post-Snowden era: On one hand, it is generally accepted that unauthorised use, storage and processing of the user's private data by the state directly clashes with our fundamental rights for privacy; on the other, the user seems to be keen on self-recording and storing one's own data by willingly using sensors, enabling him to learn more about one's habits, general health status or even personality. In the era of wearable tech we seem to be accepting that measuring data is not a privacy infringement but a self-surveillance exercise in a quest to get to know ourselves better, most acute to exercising one's right to free expression. Yet, how is this addressed in legal terms? The focal point for this paper is to address the nascent phenomenon of users actively partaking in the QS movement by wilfully sharing health related datasets. Part 1 notes the transition from the "right to be let alone" to the right to own one's data as the underlying rational for QS: is it a form of expression regarding a tradable commodity in a free market or a matter of greater public importance? Part 2 dissects the dilemma in sharing health data for public health and/or research purposes exceeding the strict limits of private sphere. The unfortunate case of Google Health, the unconstitutional purchase of Iceland's national datasets by deCODE and the mishap of the Care.data are studied to shed light to the many faces of our Quantified Selves: Is the current legislative approach fit for facilitating the QS movement, as a type of self-expression? The paper critically examines self-measurement technologies from a legal perspective and calls for urgent reforms in self-measured data protection.

Big Data is a relatively recent phenomenon, but has already shown its potential to drastically alter the relationships between businesses, individuals, and governments. The issues surrounding privacy of the online users (Mayer-Shoenberger, Cukier 2013) and the overall ethical challenges involved (Schroeder, 2014) make big data a topical issue, especially in the aftermath of the Snowden revelations. Many organisations now control vast amounts of raw data, and those industry players with the resources to mine that data to create new information have a significant advantage in the big data market. The use of predictive analytics in processing information tracked across different platforms to identify trends in the behaviour of individuals further adds value to big data (Fotopoulou, 2014) and makes it an important asset for any commercial entity. This rapid commodification of personal data has given rise to a new approach with regard its legal protection in the era of big data: a shift from the traditional privacy protection regime to a wider protection under property law is considered by scholars as an appropriate legal response to the phenomenon of monetisation of personal data, once seen through the lens of big data (Victor, 2013). The aim of this chapter is to identify the legal grounds for the ownership of big data: who legally owns the petabytes and exabytes of information created daily? Does this belong to the users, the data analysts or to the data brokers and various infomediaries? The chapter presents a succinct overview of the legal ownership of big data by examining the key players in control of the information at each stage of the processing of big data. It then moves on to describe the current legislative framework with regard to data protection and concludes in additional techno-legal solutions offered to complement the law of big data in this respect, with a particular focus on the European context .