There is no doubt that Online Social Networks have changed the way people communicate and interact. These platforms have helped to break down barriers and to ease worldwide communication, to share multimedia content or to easily know what is happening in almost any point in the world. Facebook (2004) or Twitter (2006) but also other social media platforms such as Youtube (2005) or messaging services such as WhatsApp (2009) have promoted this new era. However, the meteoric rise of the use of these platforms has also been followed by continuous attempts to subvert their original purposes, using them by the so-called malicious actors for evil purposes. Examples of this can be the generation of mistrust against vaccines, the creation of content supporting climate denial theories, or disinformation campaigns trying to manipulate people's opinion to alter the results of democratic elections. Therefore, there is a wide variety of different types of attempts to undermine social networks and to distorse public discourse. In this project, we seek to reveal and detect the presence of malicious actors in Online Social Networks and to profile these actors through a multidisciplinary approach, including experts in the use of novel computational techniques from the Artificial Intelligence and Computer Vision fields and experts from the Behavioural Sciences field. The project will also leverage a multimodality approach, analysing text, images, videos, audio network structures, interactions between users and trust perceptions. From the Behavioural Sciences research field, different approaches, including psychology, discourse analysis, sociology and human-computer interaction will allow us to build a taxonomy of the different malicious actors and to profile them. From the Artificial Intelligence field, tools such as Deep Learning, advanced Natural Language Processing, and Social Network Analysis provide us with the necessary instruments to build a software tool for the analysis and detection of these malicious actors with novel features such as multilingualism, explainability, and a strong focus on an open-source solution.

BIO-INTEL-MOB aims to revolutionize peri-urban mobility, logistics, and governance through AI-driven, citizen-centric, and climate-neutral solutions. Addressing fragmented mobility networks, inefficient logistics, and governance gaps, the project integrates Advanced Mobility Data Space (aMDS), Peri-Urban Mobility Hubs (PUMHs), Green-Safe Routing Algorithm (GSRA), bio-packaging logistics units (bioPLU), and Digital Twin ecosystems. By leveraging AI-powered risk detection, emission monitoring, real-time analytics, multimodal transport optimization, and VR/AR-enabled participatory governance, BIO-INTEL-MOB enhances urban-peri-urban accessibility and connectivity, reduces private car dependency by 35%, and optimizes multimodal transport efficiency by 35%. The project’s interventions will lead to a 30% reduction in urban-peri-urban congestion, a 25% decrease in logistics-related emissions and packaging waste. Further, BIO-INTEL-MOB will cut VRU-related accident risks by 35%, improve air quality and noise pollution by 20%, and reduce the human health impact of transport-related pollution by 30%. The project conducts large-scale pilot demonstrations in Rome, Cascais, Riga & Vilnius, alongside satellite pilots in Melsungen, Ciampino, Urla, and Rhodes, ensuring scalability and transferability. Through policy-aligned smart city governance (SCGo) with policy engine, citizen-voice-app and knowledge-hub, and AI-driven multimodal transport innovations, BIO-INTEL-MOB strengthens citizen participation in city planning by 40%, creating data-driven and participatory ecosystems. The project’s AI-driven models will ensure a 40% increase in participatory governance efficiency, 25% faster policy compliance processes, and a 35% improvement in co-designing of city policies and infrastructure. The outcomes contribute to the EU Green Deal, Digitalization Strategy, and Climate-Neutral Cities Mission, positioning peri-urban areas as resilient, connected, and low-emission living spaces.

The objective of HARNESS is to train a multidisciplinary cohort of 13 PhD students in an international Doctoral Network, specialising in ethics, law, and technology. The network will offer comprehensive training, supervision, and secondments, allowing individuals to become experts in their specific disciplines while gaining practical knowledge across various fields. The program is supported by an excellent team of partners who are world leaders in their respective domains. The ethical and socio-technical impacts of the current and forthcoming AI and data-intensive information systems (e.g. Common European Data Spaces) require careful multidisciplinary consideration and foresight techniques regarding their alignment with EU's values, sovereignty and economic and societal goals. The new European regulations on data and AI should be supported by robust methods and tools to empower citizens and civil society to swiftly comprehend, navigate, and address emerging threats to fundamental rights. HARNESS will analyse the joint impact of new AI and data-intensive technologies and their regulation, considering legal entanglements, new legal compliance tools and the consequences that can be anticipated, from ethical, societal and economic perspectives. AI and data legislation will be formalised and operationalised under the 'Law as Code' paradigm using international standards and semantic web technologies. Specific cases such as AI Foundational models, impact of new regulations on competitiveness and the interplay of self-sovereign identity in Common European Data Spaces will be analysed in detail. HARNESS will utilise and build upon the state of the art in open information systems and knowledge engineering to develop appropriate methodologies, tools, and taxonomies to assist with regulatory compliance, deliberation and agreement on ethical norms. This will involve charting the constellation of possible systems, anticipating new risks and developing effective mitigations.