Long Pham

This study examines the key factors that affect Europeans’ reactions to artificial intelligence (AI) in the context of both full and flawed democracies in Europe. AI applications have increasingly been integrated into democratic practices, ranging from micro-targeting of voters to election information campaigns and protests, as well as various administrative functions and services provided by governments. However, the impact of AI on democracy and democratic institutions has yielded mixed outcomes. Drawing upon a dataset of 4004 respondents, categorised into full democracies and flawed democracies based on The Democracy Index developed by the Economist Intelligence Unit (EIU), this research identifies crucial factors that shape Europeans’ attitudes toward AI in these two types of democracies. The analysis reveals noteworthy findings. First, flawed democracies tend to exhibit higher levels of trust in government entities compared to their counterparts in full democracies. Furthermore, individuals residing in flawed democracies demonstrate a more positive attitude toward AI when compared to respondents from full democracies. However, the study does not find significant differences in AI awareness between the two types of democracies, indicating a similar level of general knowledge about AI technologies amongst European citizens. Moreover, the study reveals that trust in AI measures, specifically “Trust AI Solution,” does not vary significantly between full and flawed democracies. This suggests that despite differences in democratic quality, both types of democracy have similar levels of confidence in AI solutions. Furthermore, employing regression models, the study uncovers the relative impact of these key factors and their correlations can reflect on policy implications. These findings contribute to a better understanding of the factors that shape the reactions of Europeans to AI in the democratic context, providing valuable information to policymakers and stakeholders in designing effective AI governance frameworks and strategies.

The article focuses on the study of workplace learning for hospital’s staff to become mortuary care agents. It is based on the analysis of verbalizations obtained from self-confrontations interviews made with the mortuary care trainees. These interviews are extracted from video recordings taken while trainees carry out their normal task. The goal is to identify, within the context of the interaction between trainees and tutors, signs of learning progress in the trainees’ verbalizations, which could be linked to existing hospital standards. As demonstrated by the activity clinic methodology, these verbalizations are not just a simple depiction of the work activity as it happened but serve as an experiment to underline the learning benefits for the trainees. Beyond the benefits of better understanding the trainees work tasks, the resulting analyses indeed support the formative aspect of self-confrontation interviews. In this perspective, the article focuses on a methodological aspect, the data analysis to account for signs of learning such as verbalized and recognized by the trainees in their interactions with tutors. On the occasion, we question the formative dimension of this methodological setting in the prospect of designing training devices for these professionals.