Getting ready for Generation Z students - considerations on 3D printing curriculum

Across the European Union, considerable discrepancies can be observed regarding the current state of AI adoption in the public sector and the complexity of functioning AI governance structures. This can be attributed to diverse levels of digitalisation, AI maturity and governance styles across EU member states. In the field of AI implementation and AI governance models in the public sector the frontrunner is the Netherlands, scoring first in the Global Index on Responsible AI. Analysing this example of good practices in terms of AI governance, with a focus on the delegation acceptance perspective, is of relevance for the state of art on AI governance within the EU. The article looks into the structure of the public Dutch Algorithm Register which currently contains over 400 entries, the AI framework for the public sector, supervisory structures in place and risks management approaches, addressing the importance of values in the development and deployment of AI systems and algorithms. The article demonstrates how in the case of AI also, early adaptors shape future behaviours, thus carrying a burden of responsibility when developing and deploying key enabling technologies in line with the core values.

This article presents the results of a multilayered exploratory investigation on the perception, consumption, and regulation of artificial intelligence-based solutions and the experienced understanding of the technology's development and deployment. This research probes the understanding of concepts such as trustworthy and human-centric AI, the criteria that are used when selecting a certain AI system, and its perceived risks. A multilayered exploratory investigation was conducted, including desk research and qualitative input collection. An AI literacy deconstruction exercise was conducted, mapping the literacy needs and the risks associated with low AI literacy levels for three roles interacting with an AI system-developer, deployer and affected person/user, in response to the AI literacy obligations of the European Artificial Intelligence Act. AI literacy programmes must be role specific and calibrated to function and organisational needs. Transversal skills and knowledge are found in all roles, such as critical thinking, needed to correctly interpret the results. The affected citizen/ user also must have a wide range of skills and knowledge of how AI systems function, what their limitations are, the legislation addressing AI systems and ethical values and principles that should be embedded in these systems. It is not low AI literacy in absolute terms that is risky, but rather unfounded perception of one's own high AI literacy and overreliance on AI systems in the absence of critical thinking. AI systems are perceived in a dystopian manner due to their risks and the attention inflation they receive, at the expense of other topics.

Performance-based research policies and programmes have fundamentally altered both organisational and individual behaviours and expectations, putting immense pressure on researchers. The soundness of research, originality, valorisation potential, and societal and economic impacts are highly valued and expected characteristics of research. Yet, our understanding of the effects of various systemic and organisational factors on research performance is limited. In an exploratory, single-country case, this paper aimed to develop and examine different models of research performance as perceived by researchers themselves using a large cross-disciplinary sample of 553 researchers from 72 public research organisations in Romania. A pre-tested questionnaire was self-administered online, comprising seven scales: (1) recruitment and selection, (2) research recognition and value, (3) participation in research projects and teams, (4) work incentives, (5) job payment and salary, (6) career development opportunities, and (7) leadership effectiveness. Maximum likelihood and Bayesian estimators were used to test three structural models: (M1) mono-factor; (M2) intercorrelated dimensions, and (M3) the dimensions are indicators of a general construct. Additionally, a path analysis was carried out to study the relationships among the dimensions. We found that M2 and M3 fit the empirical data better. The results showed that career development programmes and opportunities gain centrality in achieving research performance by directly influencing participation and research projects and teams and mediating the effect of job payment. Revealingly, powerful work incentives within research organisations are international mobilities or appreciation awards. When informing evidence-based policies, the models we propose could serve the goal of improving research performance through talent development as the main proxy.

The AI “oversight – control” nexus is a matter of debate in both scientific literature and policy papers, given the complex and disruptive nature of the technology and the intricate legislative systems regulating the deployment of AI systems. Most approaches give precedence to the need for a multilayered governance model, while in the same time taking into account the lifecycle stages of the AI product. Oversight over the ecosystem in which the AI is deployed (the governance layer) should not be confused with the process level human oversight. At process level, human oversight is needed in the case of “high-risk systems”, and AI literacy is a precondition for this oversight to be relative. Effective oversight requires many resources at both ecosystem and organisational levels. Additionally, oversight of AI systems is also highly regulated in democratic societies and therefore can be seen as a reflection of the importance that democratic values have in the way disruptive technologies are deployed within society.