Search

We shall first introduce some technical aspects involved in the use of AI in producing new intellectual works, distinguishing approaches based on knowledge representation and on machine learning. Then we shall provide an overview of some of the significant applications of AI to the production of intellectual work, distinguishing the extent to which they depend on pre-existing works, and the different ways in such works are used in the creative process. Finally, we shall discuss the legal aspects of AI-reuse of copyrighted works, focusing on the rights of the authors of such works relatively to the process and the outputs of AI.

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.