funding
Horizon Europe Call
CERTAIN was funded under Horizon European Call HORIZON-CL4-2024-DATA-01-01: AI-driven data operations and compliance technologies (AI, data and robotics partnership).
The projects funded under this call are expected to contribute to the following outcomes:
- To enable companies and public sector to easily comply with existing and emerging regulation (e.g. GDPR, Data Governance Act, Data Act, Artificial Intelligence Act) and create value on data assets that they possess or that they acquire from the market, and to allow citizens to feel more confident that data-driven systems treat them in a fair, unbiased and compliant way and respect their privacy/anonymity and other rights, and keep track of the use of personal data in a world where “everything” moves online.
- Define, quantify and measure bias in data sets (especially those used for AI development).
- Shorten the time-to-market and reduce development costs of compliant data solutions
- Contribute to open, trusted and federated Common European data spaces.
- Quantify and reduce the environmental footprint of data operations which will contribute to the Green Deal target “no net emissions of greenhouse gases by 2050”.
Other projects funded
Three other projects were funded under the same call, with whom CERTAIN has close ties and active synergies.
DataPACT is a Horizon Europe-funded project launched in 2025, uniting 18 partners from 16 countries, including universities, research centers, SMEs, public organizations, and large enterprises. Coordinated by SINTEF AS, it integrates compliance, ethics, and sustainability into data and AI pipelines. DataPACT develops a Compliance Toolbox, Framework, and Pipeline Toolbox to ensure regulatory adherence. The project is validated through seven use cases in healthcare, smart cities, law enforcement, media, CRM, manufacturing, and public data, fostering responsible AI adoption in Europe.
ACHILLES aims to create an efficient, compliant, and ethical AI ecosystem, addressing challenges related to privacy, security, fairness, and transparency. The project proposes an terative development cycle inspired by clinical trials, consisting of four modules focused on human-centric, data-centric, model-centric, and deployment-centric strategies.This approach seeks to enhance the performance and reliability of AI systems while ensuring compliance with legal and ethical standards. A key innovation is the development of a machine learning-driven Integrated Development Environment (IDE), which will streamline the integration between modules and promote the creation of responsible AI solutions.