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.

The mission of the EIT Community Strategic Synergies (EIT CSS) is to facilitate and foster collaboration across EIT Community in key thematic areas identified in the EIT SIA 2021-207 and EU policy priorities. By bringing together and promoting cooperation across the different KICs involved, the EIT SCC elevates the EIT Community’s visibility and impact in key areas to drive a systemic change in Europe. By gathering EIT Community’s answers to strategic challenges in one cluster, the EIT CSS creates links between programs that would otherwise never meet, it increases scalability of actions and results, it improves efficiency in terms of management and implementation of processes, and attract new sources of fundings. The EIT CSS builds on the diverse expertise and knowledge of the KICs involved and, inspired by the holocaracy management framework yet aligned with EIT’s recommendations, it positions itself as unique community to address some of today’s most pressing challenges for Europe in four areas: : 1. Artificial Intelligence: by providing solutions for a human-centric implementation of AI that can best address its societal, business, environmental and regulatory challenges but also contributing to the ambitious Deep Tech Talent Initiative (DTTI) initiave; 2. New European Bauhaus (NEB)by supporting community-based capacity building initiatives and start-ups to accelerate the shift towards a more sustainable, inclusive, and beautiful Europe and contributing therefore to the Green Deal; 3. Women Entrepreneurship: by promoting a more inclusive entrepreneurial ecosystem, mainstreaming business opportunities and increased the share of women-led innovation in the market; 4. Circular Economy: facilitating the shift from a linear to a circular economy model by connecting and engaging the different stakeholders of the innovation ecosystem.

By building on the recommendations of the working group between EIC and EIT boards, the LoI between the 1st wave EIT KICs and EIC and the MoU between EIT and EIC, this proposed initiative aims at piloting mechanisms by which the most innovative businesses stemming from EIT Digital can be channeled to the Enhanced EIC Pilot, creating a pipeline of impactful innovations. In addition, the action will allow EIC-funded innovators to benefit from the support of the EIT Digital Accelerator and Innovation Factory. The collaboration will be along the following lines: 1. EIT Digital Accelerator: (i) EIC Accelerator scaleup systematic access to the EIT Digital Accelerator: base fee (50k) will be 100% discounted for selected EIC Accelerator-funded scaleups signing up during the pilot. This will guarantee scaleups having received EIC Accelerator funding, carefully selected by EIC based on excellence and growth potential (Seal of Excellence), to further grow with the support of EIT Digital and its network of corporates and investors. (ii) EIC investment in scaleups selected and coached by EIT Digital Accelerator would guarantee an intake of DeepTech scaleups with high-growth potential to the EIC and link them to a strong ecosystem of complementary investors. 2. EIT Digital Innovation Factory: (i) EIC grants and investments in ventures and activities of the EIT Digital Innovation Factory would further support innovative startups, maximize the outcome of EIT Digital and its strong partner ecosystem and ultimately build a strong portfolio of European centaurs and unicorns; (ii) Channel EIC Pathfinder innovators to EIT Digital Innovation Factory to create and validate strong business plans and build ventures to bring their solutions to the market. This will allow to build and grow European DeepTech ventures and help them succeed in the market. The ultimate goal of the collaboration will be to enhance European support to innovators in alignment with EC's priorities.

The mission of BDVe is to support the Big Data Value PPP in realizing a vibrant data-driven EU economy or said in other words, BDVe will support the implementation of the PPP to be a SUCCESS. Behind that mission, there are multiple goals to achieve, which should be taken into full consideration when defining the directions of the PPP. Some of the most challenging ones are: (1) achieving a more competitive landscape of European Big Data providers, leading to bigger market share; (2) creating the context for a more competitive EU industry (transport, manufacturing, public sector, agrifood, media, energy…) in the advent of a data-driven revolution where many traditional players will have to transform their processes and re-think their business if they want to remain completive –or in some cases, just to survive-; (3) ensuring the sustainability of the investments and actions triggered by the PPP. BDVe has broken down those high-level goals into 7 major priorities for the project: • Being accurately informed about most important facts in Big Data so that we have a solid basis to support the decision-making process in the PPP • Supporting the implementation of the Big Data PPP from an operational point of view • Developing a vibrant community around the PPP • Supporting the development of a European network of infrastructures and centers of excellence around Big Data • Setting-up a professional Communications strategy • Setting up a framework that supports the acceleration of data-driven businesses, and • Ensuring the sustainability of the investments and actions triggered by the PPP. The BDVe consortium includes a set of partners that have shown commitment and dedication to the success of the PPP for several years. They have already invested and they have committed to invest along the coming years. We believe that this CSA cannot be a neutral action that offers operational support without further commitment.

MANOLO will deliver a complete stack of trustworthy algorithms and tools to help AI systems reach better efficiency and seamless optimization in their operations, resources and data required to train, deploy and run high-quality and lighter AI models in both centralised and cloud-edge distributed environments. It will push the state of the art in the development of a collection of complementary algorithms for training, understanding, compressing and optimising machine learning models by advancing research in the areas of: model compression, meta-learning (few-shot learning), domain adaptation, frugal neural network search and growth and neuromorphic models. Novel dynamic algorithms for data/energy efficient and policy-compliance allocation of AI tasks to assets and resources in the cloud-edge continuum will be designed, allowing for trustworthy widespread deployment. To support these activities a data management framework for distributed tracking of assets and their provenance (data, models, algorithms) and a benchmark system to monitor, evaluate and compare new AI algorithms and model deployments will be developed. Trustworthiness evaluation mechanisms will be embedded at its core for explainability, robustness and security of models while using the Z-Inspection methodology for TrustworthyAI assesment, helping AI systems conform to the new AI Act regulation. MANOLO will be deployed as a toolset and tested in lab environments via Use Cases with different distributed AI paradigms within cloud-edge continuum settings; it will be validated in verticals such as health, manufacturing, and telecommunications aligned with ADRA identified market opportunities, and with a granular set of embedded devices covering robotics, smartphones, IoT as well as using Neuromorphic chips. MANOLO will integrate with ongoing projects at EU level developing the next operating system for cloud-edge continuum, while promoting its sustainability via the AI-on-demand platform and EU portals.