Fostering Innovative and Compliant Data Ecosystems (IA) (AI, Data and Robotics Partnership)

Develop innovative solutions to streamline compliance processes and enhance competitiveness within the EU.

Generate and utilize synthetic data to enhance data quality, diversity, and representativeness, making it a crucial tool for AI-powered innovation and regulatory compliance.

Address cybersecurity, interoperability, reproducibility and standardization, and/or liaise with other actions working on those aspects, in view of facilitating effective data sharing across platforms and sectors, while ensuring an adequate level of security and protection.

Provide necessary comprehensive user training and support (also involving the users/stakeholders outside the project), ensuring adaptability and scalability to accommodate evolving regulations and diverse organizational needs and to raise awareness and improve understanding of relevant compliance issues.

Analyse and address the real needs of real users and stakeholders, and how these will be addressed in the proposed action.

Link the training and user needs to tangible progress indicators in the proposal.

Area 1: Actions to develop advanced compliance technology integrating AI, cybersecurity, language technologies, and privacy preservation. This framework could include the creation of NLP [2] -driven semantic analysis tools for deciphering complex legal texts and translating them into clear compliance tasks, energy-efficient neuromorphic approaches and mechanisms for optimising massive data operations, or machine learning algorithms trained on historical data to predict and mitigate potential compliance violations. With the capability to detect changes in EU legislation, these advanced AI systems and analytics tools will provide deep insights into compliance performance, risk management, and help forecast upcoming regulatory trends to strategically prepare for future requirements. For usability, it is also important that the tools can be integrated with the organisation’s existing processes and systems.

Area 2: Actions to ensure auto-compliance of data transactions and data spaces with applicable regulation (e.g. data and sectoral legislation). Actions in this area should anticipate compliance tasks within the context of Common European Data Spaces and coordinate with them as necessary. Actions in this area are expected to develop automatic or semi-automatic tools that analyse and take into account the specific architecture, governance model, exchange mechanisms, tools, data types, identity management, smart contracting, user policies and other user needs or operational features of the actual data spaces, liaising with and building on other actions working in this area, in particular the Data Spaces Support Centre.

Area 3: Actions to generate, manage and leverage sy nthetic data in order to improve data quality, availability, representativity, fitness for purpose and compliance. The actions should in particular address the inherent shortcomings of real world data that would necessitate synthetic data (e.g. data availability, confidentiality, privacy protection, enhancing quality, diversity, representativeness, bias). Additionally, actions may target generating synthetic data for sparse or unusual domains, integrating synthetic and real data effectively, or advancing technological capabilities in generative models and simulation-based approaches to drive synthetic data generation forward and/or addressing or modelling rare events and complex dynamic systems. All actions under this Area are expected to address the evaluation, validation and benchmarking of synthetic data to ensure fitness for purpose and safe, ethical and compliant use of synthetic data, including the analysis and mitigation of biases inherited from the original data or introduced by the synthetic data generation process. For these purposes, collaboration with simulation/digital twins actions could be explored.