Along the whole value chain in using data for economic purposes, guidelines and tools are required to make the business of the different stakeholders successful, and the end-users confident that none of their rights are endangered. CERTAIN addresses these needs and delivers solutions for data holders, dataspaces and AI systems providers, and AI systems deployers, which are the primary actors of the data and AI value chain. They must be compliant with applicable European regulations, must reach this compliance in a timely manner, and at reasonable cost. CERTAIN delivers guidelines and technical tools to help with compliance, to assess data quality, to measure biases in datasets, and to protect privacy. CERTAIN sets the foundation of AI certification: it translates the regulations to business terms, builds a directory of certification entities per business, develops a platform to streamline the certification process, and tools for AI system providers and certification entities so that they could respectively prepare and run a certification process. In case of security breach, not only privacy may get compromised, but also AI models may become useless and lead to extremely damageable decisions. To make sure that AI-based products are of high quality and reliability, CERTAIN develops security tools and methods, specifically suitable for dataspaces and AI systems. CERTAIN addresses the environmental footprint of the AI value chain. Innovative techniques are elaborated to reduce energy consumption when building and running AI systems. This is beneficial not only for the green deal but to reduce cost for AI stakeholders. As importantly, CERTAIN considers the end-users perspective, and provides templates and guidelines that may be used by AI systems deployers to reassure end-users on the use of their private data. The project tests its results on seven operational pilots in six different business areas, considering all the actors along the AI value chain.

PALIMPSEST takes inspiration from the original meaning of the Greek word παλίμψηστος (palimpsestos, ‘again’ + ‘scrape’), which describes the process of the writing practices over papyrus: existing text was scraped and washed off, the surface re-smoothed, and the new literary material written on the saved material. PALIMPSEST adopts this re-writing perspective and grounds it on a living heritage approach. PALIMPSEST envisages regenerating the lost “sustainability wisdom” underlying the production of heritage landscapes through the activation of co-creation processes involving creative actors, technical stakeholders and civic society. Here architecture, design and art practices will dialogue with place-specific needs and broad systemic challenges to imagine new scenarios and experiment with innovative practices connecting human actions, landscape heritage and sustainability objectives. Such experiments will envision novel Landscape Scenarios aiming at producing dedicated Landscape Services, inspired by the generation of beneficial outcomes on ecosystem functions, which the creative contribution of CCIs will empower. Human practices will arise as relevant agents of a new sustainable palimpsest process. PALIMPSEST will integrate the aforementioned Landscape Services in environmental-sensitive solutions with sustainable finance infrastructures to support the sharing and circulating of positive externalities at different levels among the landscape service actors and communities. PALIMPSEST revolves around three pilots with strong cultural identities and relevant environmental problems: Lodz (PL), a UNESCO city of films fighting the highest air pollution levels in Europe; Milan fringes (IT), traditional agricultural landscapes struggling with unsustainable water use; Jerez de la Frontera (ES), an Andalusian wine landscape and vernacular site challenged by renewable energy production facilities.

A vast amount of Earth Observation data is produced daily and made available through online services and repositories. Contemporary and historical data can be retrieved and used to power existing applications, to foster innovation and finally improve the EU citizens’ lives. However, an undersizedaudience follows this activity, leaving huge volumes of valuable information unexploited. EO4EU aims to provide innovative tools, methodologies and approaches that would assist a wide spectrum of users, from domain experts and professionals to simple citizens to benefit from EO data. EO4EU strives to deliver dynamic data mapping and labelling based on AI adding FAIRness to the system and data. EO4EU introduces an ecosystem for holistic management of EO data, bridging the gap among domain experts and end users, bringing in the foreground technological advances to address the market straightness towards a wider usage of EO data. EO4EU envisages to boost the Earth Observation data market, providing a digestible data information modeling for a wide range of EO data, through dynamic data annotation and a state-of-the-art serverless processing by leveraging important European Cloud & HPC infrastructures.

The AIXPERT project proposes a novel, comprehensive approach to developing explainable, accountable, and transparent AI systems. At its core, the project introduces an adaptable, situation-aware AI-agentic platform capable of encapsulating various AI models, regardless of their underlying architecture. This architecture-agnostic approach significantly enhances the trustworthiness of AI systems by providing a consistent framework for explainability and accountability across different model types. The project aims to address challenges in AI explainability, transparency, accountability, autonomy, and robustness by integrating multi-agent systems with multimodal foundation models and leveraging real-time human feedback. This integration enhances AI system trustworthiness and user-friendliness while maintaining flexibility in the choice of underlying AI models. The project will be implemented in a multi-layered architecture, with each layer focusing on specific aspects of AI interpretability and explainability: the Agent-World Interface Layer will define and coordinate AI agents, their situational awareness, and their interactions with real-world knowledge sources; the Dialogue Mediation Layer will control user-agent and agent-agent communications; and the Cognitive Foundation Layer will provide the base capabilities for the system based on explainable multimodal foundation models. The project will also focus on developing a framework for assessing AI trustworthiness, demonstrating the value of explainable AI through pilot demonstrations in healthcare, recruitment services, manufacturing, educational robotics, and creative industries, and ensuring the sustainability of its results. AIXPERT envisions delivering AI solutions that are not only transparent and ethical but also sustainable and adaptable to diverse user needs, ultimately fostering greater trust in AI across multiple industries and societal sectors.

DIOPTRA aims to introduce a front-line screening tool that will consider risk factors and protein biomarkers for pinpointing individuals at a high risk for colorectal cancer (CRC) incidence. Tissue & blood samples will be examined towards a discriminative set of prognostic proteins that are detectable via standard bloodwork and can indicate a need for further evaluation (i.e. colonoscopy). Other data (e.g. medical, behavioural) will also be considered as potential risk factors. Artificial intelligence (AI) will be leveraged for assessing prognostic power, while personalised behavioural change will be promoted based on modifiable risk factors. Given the low citizen participation on CRC screening across EU, DIOPTRA seeks to broaden the evaluated population, boosting participation rates and bypassing age screening thresholds. This action is part of the Cancer Mission cluster of projects on ‘Prevention, including Screening’.