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This report contains preliminary results of the study aiming at automating legal evaluation of privacy policies, under the GDPR, using artificial intelligence (machine learning), in order to empower the civil society representing the interests of consumers. We outline what requirements a GDPR-complaint privacy policy should meet (comprehensive information, clear language, fair processing), as well as what are the ways in which these documents can be unlawful (if required information is insufficient, language unclear, or potentially unfair processing indicated). Further, we analyse the contents of privacy policies of Google, Facebook (and Instagram), Amazon, Apple, Microsoft, WhatsApp, Twitter, Uber, AirBnB, Booking.com, Skyscanner, Netflix, Steam and Epic Games. The experiments we conducted on these documents, using various machine learning techniques, lead us to the conclusion that this task can be, to a significant degree, realized by computers, if a sufficiently large data set is created. This, given the amount of privacy policies online, is a task worth investing time and effort. Our study indicates that none of the analysed privacy policies meets the requirements of the GDPR. The evaluated corpus, comprising 3658 sentences (80.398 words) contains 401 sentences (11.0%) which we marked as containing unclear language, and 1240 sentences (33.9%) that we marked as potentially unlawful clause, i.e. either a "problematic processing” clause, or an “insufficient information” clause (under articles 13 and 14 of the GDPR). Hence, there is a significant room for improvement on the side of business, as well as for action on the side of consumer organizations and supervisory authorities.

Accidents involving autonomous vehicles (AVs) raise difficult ethical dilemmas and legal issues. It has been argued that self-driving cars should be programmed to kill, that is, they should be equipped with preprogrammed approaches to the choice of what lives to sacrifice when losses are inevitable. Here we shall explore a different approach, namely, giving the user/passenger the task (and burden) of deciding what ethical approach should be taken by AVs in unavoidable accident scenarios. We thus assume that AVs are equipped with what we call an "Ethical Knob'', a device enabling passengers to ethically customise their AVs, namely, to choose between different settings corresponding to different moral approaches or principles. Accordingly, AVs would be entrusted with implementing users' ethical choices, while manufacturers/programmers would be tasked with enabling the user's choice and ensuring implementation by the AV.

This paper presents the first release of the Legal Case, recently developed by the ALIAS Project and still under refinement. The Legal Case is a methodological tool intended to address liability issues of automated ATM systems: it provides for a legal risk management process that can be applied either proactively or retroactively. Used in a proactive way, the Legal Case aims to address the liability issues that may emerge in the design of new technologies. Used in a retroactive way, it is meant to assess liability issues pertaining to an existing piece of technology that has already reached the deployment stage. The Legal Case is mainly designed to be used by a Legal Analyst who is a member of an interdisciplinary project team dealing with the development of new automated technologies (in the case of the proactive approach) or with the accident investigation (in the case of the retroactive approach). Although not yet finalized, the Legal Case methodology is gathering great interest from the ATM community.

Who is responsible for accidents in highly automated systems? How do we apportion liability among the various participants in complex socio-technical organisations? How can different liability regulations at different levels (supranational, national, local) be harmonized? How do we provide for accountability, while promoting safety? These and other questions are being addressed by the ALIAS (Addressing Liability Impact of Automated Systems) project. In this paper we present the outline framework of the project, its objectives, and some preliminary results: in particular, we present a framework for liability in aviation, an analysis of real accidents and of a hypothetical case involving UAS according to a methodology developed in the project, and finally, we introduce the Legal Case, that is a methodological tool (currently under development) aimed at identifying and addressing liability issues of automated ATM systems.

Who is responsible for accidents in highly automated systems? How do we apportion liability among the various participants in complex socio-technical organisations? How can different liability regulations at different levels (supranational, national, local) be harmonized? How do we provide for accountability, while promoting safety? These and other questions are being addressed by the ALIAS (Addressing Liability Impact of Automated Systems) project. In this paper we present the outline framework of the project, its objectives, and some preliminary results: in particular, we present a framework for liability in aviation, an analysis of real accidents and of a hypothetical case involving UAS according to a methodology developed in the project, and finally, we introduce the Legal Case, that is a methodological tool (currently under development) aimed at identifying and addressing liability issues of automated ATM systems.

The introduction of highly automated technologies in socio-technical systems (STS) gives rise to new legal questions, especially as concerns liability for accidents, calling for new models of allocating decision-making tasks between humans and machines. We first discuss the relation between responsibility for the execution of a task and legal liability. Then we analyse the impact of automation in the allocation of liability within STS. In this regard, we present an actor-based analysis of liability allocation, taking account of the main types of liability, and in particular liability for software failures. In the final section of the chapter, we present the Legal Case, a methodology by which to analyse liability allocation in automated STSs and to assess the resulting legal risk for all actors involved.