The paper dissects the intricacies of Automated Decision Making (ADM) and urges for refining the current legal definition of AI when pinpointing the role of algorithms in the advent of ubiquitous computing, data analytics and deep learning. ADM relies upon a plethora of algorithmic approaches and has already found a wide range of applications in marketing automation, social networks, computational neuroscience, robotics, and other fields. 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; this can take various shapes and thus yield different answers to key issues regarding agency. The paper offers a fresh look at the concept of "Machine Intelligence", 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 and can thus serve as a point of reference for outlining distinct rights and obligations of the programmer and the consumer: driverless cars are used as a case study to explore the several layers of human and machine interaction. These different degrees of automation reflect various levels of complexities in the underlying algorithms, and pose very interesting questions in terms of regulating the algorithms that undertake dynamic driving tasks. Part 2 further discusses the intricate nature of the underlying algorithms and artificial neural networks (ANN) that implement them and considers how one can interpret and utilize observed patterns in acquired data. Finally, the paper explores the scope for user empowerment and data transparency and discusses attendant legal challenges posed by these recent technological developments.

On 7th January 2013 the Anonymous hacking collective launched a White House petition asking the Obama administration to recognize DDoS 1 attacks as a valid form of protest, similar to the Occupy protests. The 'Occupy' movement against financial inequality has become an international protest phenomenon stirring up the debate on the legal responses to acts of civil disobedience. At the same time, online attacks in the form of DDoS are considered by many as the digital counterparts of protesting. While the law generally acknowledges a certain level of protection for protesting as a manifestation of the rights to free speech and free assembly, it is still unclear whether DDoS attacks could qualify as free speech. This paper examines the analogies between offline protests and DDoS attacks, discusses legal responses in both cases and seeks to explore the scope for free speech protection.

The paper discusses the theory of "propertization of data", namely the proposition that data can be owned and constitute one's property, in the light of big data and the quantified self (QS) movement. Can we really own our own data? Part 1 discusses the issue of commodification of personal data and part 2 dissects this further by examining how personal data is treated at each stage of the big data processing cycle. Parts 3 and 4 complete the argument put forth here, namely that raw data-once seen out of context and as a part of a dataset can indeed be considered as property, able to be owned and traded.

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