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In recent years, the European Union has advanced towards responsible and sustainable Artificial Intelligence (AI) research, development and innovation. While the Ethics Guidelines for Trustworthy AI released in 2019 and the AI Act in 2021 set the starting point for a European Ethical AI, there are still several challenges to translate such advances into the public debate, education and practical learning. This paper contributes towards closing this gap by reviewing the approaches that can be found in the existing literature and by interviewing 11 experts across five countries to help define educational strategies, competencies and resources needed for the successful implementation of Trustworthy AI in Higher Education (HE) and to reach students from all disciplines. The findings are presented in the form of recommendations both for educators and policy incentives, translating the guidelines into HE teaching and practice, so that the next generation of young people can contribute to an ethical, safe and cutting-edge AI made in Europe. [ABSTRACT FROM AUTHOR]

The social media debate preceding the 2016 Brexit referendum represents a yet another instance of the growing role of social networking sites, also known as social media, in steering the dynamics of the socio-political process nowadays. Considering the scale of the phenomenon as well as the variety of concerns it raises vis-à-vis transparency, equality, representation, and legitimacy of the political process, it is imperative to query the mechanisms behind the relationship that unfolds between social media, their users and the political process. To do this, this paper employs event study analysis and recent advances in data mining and data analysis to examine how certain non-virtual political events constituent of the Brexit debate had been played out in the social media realm and influenced the social media users' stance toward the very question of Brexit. This composite methodological approach that this study adopts allows to measure how non-virtual political events influenced the network of users who discussed the withdrawal of the UK from the EU in Twitter in the weeks prior to the Brexit referendum. The outcomes of this study suggest that social networking sites play a pivotal role not only on how information is diffused over the network but also on user's message creation, dissemination behaviour and the shape of the social network itself. [ABSTRACT FROM AUTHOR]

Intent classification and sentiment analysis stand as pivotal tasks in natural language understanding (NLU), with applications ranging from virtual assistants to customer service. The advent of transformer-based models has significantly enhanced the performance of various NLP tasks, with encoder-only architectures gaining prominence for their effectiveness. More recently, there has been a surge in the development of larger and more powerful decoder-only models, traditionally employed for text generation tasks. This paper aims to answer the question of whether the colossal scale of newer decoder-only language models is essential for real-world applications. The investigation involves a performance comparison between these decoder-only models and the well-established encoder-only models specifically in the domains of intent classification and sentiment analysis. The results of our study indicate that, for tasks involving natural language understanding, encoder-only models generally outperform decoder-only models, all while demanding a fraction of the computational resources. This sheds light on the practicality and efficiency of encoder-only architectures in comparison to their decoder-only counterparts in real-world applications, providing valuable insights for the advancement of natural language processing technologies. [ABSTRACT FROM AUTHOR]