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.

Despite rising investments on BigData, AI and IoT in finance/insurance, financial and insurance organisations are still facing many challenges (i.e. data “silos”, regulatory complexity, lack of testbed resources) in realizing the full potential of these technologies. INFINITECH is a joint effort of global leaders in ICT and finance towards lowering the barriers for BigData/IoT/AI driven innovation, boosting regulatory compliance and stimulating additional investments. It will provide: 1)Novel BigData/IoT technologies for seamless management and querying of all types of data (e.g., OLAP/OLTP, structured/unstructured/semi-structured, data streaming & data at rest), interoperable data analytics, blockchain-based data sharing, real-time analytics, as well as libraries of advanced AI algorithms. 2)Regulatory tools incorporating various data governance capabilities (e.g. anonymization, eIDAS integration) and facilitating compliance to regulations (e.g., PSD2, 4AMLD, MIFiD II). 3)Nine novel and configurable testbeds & sandboxes, each one offering Open APIs and other resources for validating autonomous and personalized solutions, including a unique collection of data assets for finance/insurance. The project’s results will be validated in the scope of 14 high impact pilots providing complete coverage of the sectors, including Know Your Customer (KYC), customer analytics, personalized portfolio management, credit risk assessment, preventive financial crime analysis, fraud anticipation, usage based insurance, agro-insurance and more. INFINITECH will establish a market platform that will provide access to the project’s solutions, along with a Virtualized Digital Innovation Hub (VDIH) that will support innovators (FinTech/InsuranceTech) in their BigData/AI/IoT endeavors. Based on their strong footprint in the European digital finance ecosystem, the partners will engage stakeholders from all EU-28 countries, making INFINITECH synonymous to disruptive BigData/AI innovation in the target sectors.

The reliable application of LLM-based agents to SE requires a tremendous increase in their accuracy and minimisation of their bias. While LLMs continue increasing in size and performance, it seems that phenomena like hallucinations of a single agent are substantially inevitable, since they are linked to the fundamental inference mechanism in generative models. On the other hand, evidence starts accumulating about the possibility of achieving the required performance by collaboration and debate among groups of agents. As it happens among humans, quality of work increases with specialisation of workers on tasks, organised collaboration, and discussion among workers with different backgrounds. Differently from humans, the instantiation of multiple required AI agents, and the collaboration and discussion among them, are very fast and cheap, making this approach particularly convenient. MOSAICO proposes the theoretical and technical framework to implement this approach and to scale it to very large groups of collaborating agents, i.e. AI-agent communities. The developed solutions are composed into an integrated MOSAICO platform, handling communication, orchestration, governance, quality assessment, benchmarking and reuse of AI agents. MOSAICO is integrated with existing development environments, to present the results to software engineers, and allow expert users to intervene in the AI decisions. The performance and reliability of MOSAICO technologies and tools to achieve given software engineering tasks are assessed within 4 different use cases scenarios coming from immersive technologies, bank/financing, aerospace and Internet of Things sectors. The long-term adoption of MOSAICO results and technologies will be ensured by open sourcing the code and fostering an open collaboration, such as open-source initiatives, to enhance user engagement in the MOSAICO community.