LNETN responds to MSCA ETN objectives and addresses the urgent need for new legitimation perspectives, theories, approaches and methods to learn and know how best to interpret and respond to newness, committing to and ensuring a positive social impact. It is the first ETN that combines newness, legitimation and theory building to address the need for training of a new generation of ESRs with quadruple-i (inter-disciplinary, inter-sectoral, inter-technology and international) and transferable knowledge, skills and broad vision. LNETN timely sets up a unique, innovative quadruple-i research and training network within social sciences and economics, with applications in science, engineering, technology, health and humanities to provide a high quality quadruple-i research platform for the training of 15 ESRs in legitimation of newness and theory building focusing on interpretation and understanding of complex phenomena in ways which will transform effective decision making and policy implementation and contribute to economic development. The overall objective and main objectives are fulfilled by the following specific research objectives: link the dimensions of sustainability to the discussion of tomorrow’s leadership and organisation, connected to a discussion of qualitative and ethical organization; generalise into a widely applicable theory/policy/practice and provide high quality research data on how new industries and ventures gain legitimation; evaluate innovative structural and operational organisational typologies and identify best practices; create a substantial, path-breaking direction for studying newness in seemingly opposing social and ethical settings. LNETN will be implemented by strong international consortium of 4 universities from Europe, 1 from US, an NGO, 4 innovative, high-technology companies and 3 innovation hubs. LNETN consortium provides for quadruple-i mobility pathways, coupled with development of research-related and transferable competences.

The XPM project aims to integrate explanations into Artificial Intelligence (AI) solutions within the area of Predictive Maintenance (PM). Real-world applications of PM are increasingly complex, with intricate interactions of many components. AI solutions are a very popular technique in this domain, and especially the black-box models based on deep learning approaches are showing very promising results in terms of predictive accuracy and capability of modelling complex systems. However, the decisions made by these black-box models are often difficult for human experts to understand – and therefore to act upon. The complete repair plan and maintenance actions that must be performed based on the detected symptoms of damage and wear often require complex reasoning and planning process, involving many actors and balancing different priorities. It is not realistic to expect this complete solution to be created automatically – there is too much context that needs to be taken into account. Therefore, operators, technicians and managers require insights to understand what is happening, why it is happening, and how to react. Today’s mostly black-box AI does not provide these insights, nor does it support experts in making maintenance decisions based on the deviations it detects. The effectiveness of the PM system depends much less on the accuracy of the alarms the AI raises than on the relevancy of the actions operators perform based on these alarms. In the XPM project, we will develop several different types of explanations (anything from visual analytics through prototypical examples to deductive argumentative systems) and demonstrate their usefulness in four selected case studies: electric vehicles, metro trains, steel plant and wind farms. In each of them, we will demonstrate how the right explanations of decisions made by AI systems lead to better results across several dimensions, including identifying the component or part of the process where the problem has occurred; understanding the severity and future consequences of detected deviations; choosing the optimal repair and maintenance plan from several alternatives created based on different priorities; and understanding the reasons why the problem has occurred in the first place as a way to improve system design for the future.

<< Objectives >>Climate Change is the “Biggest Threat Modern Humans Have Ever Faced’ (UN, 2021). The European Commission calls to prioritise environmental sustainability in members’ education systems. Despite the urgency of the problem, effective programs on climate change are scant. The proposed project aims to develop an interactive digital educational programme, for higher education to promote pro-environmental behaviour and support EU’s Green Deal, as well as its objective to promote digital literacy.<< Implementation >>The project will develop an intelligent educational video selection and content creation algorithm to support the development of an interactive video-based educational platform (IEP), the BTheChange, on climate change. Educational content will be individualised and matched to user preferences resulting in a powerful, appealing and engaging learning experience for the higher education learner. Effective educational videos will be reinforced with impactful learning and behaviour change activities.<< Results >>The project will result in a digital and video-based educational platform delivering a course on climate change for higher education learners. It is expected to promote knowledge acquisition, reinforce pro-environmental attitudes and behaviours. It is also expected to promote participants’ responsible digital citizenship. BTheChange methodology is expected to result in a novel methodology for the development of impactful and thought-provoking digitally delivered educational content.