Artificial Intelligence (AI) is increasingly employed by businesses, governments, and other organizations to make decisions with far-reaching impacts on individuals and society. This offers big opportunities for automation in different sectors and daily life, but at the same time it brings risks for discrimination of minority and marginal population groups on the basis of the so-called protected attributes, like gender, race, and age. Despite the large body of research to date, the proposed methods work in limited settings, under very constrained assumptions, and do not reflect the complexity and requirements of real world applications. To this end, the MAMMOth project focuses on multi-discrimination mitigation for tabular, network and multimodal data. Through its computer science and AI experts, MAMMOth aims at addressing the associated scientific challenges by developing an innovative fairness-aware AI-data driven foundation that provides the necessary tools and techniques for the discovery and mitigation of (multi-)discrimination and ensures the accountability of AI-systems with respect to multiple protected attributes and for traditional tabular data and more complex network and visual data. The project will actively engage with numerous communities of vulnerable and/or underrepresented groups in AI research right from the start, adopting a co-creation approach, to make sure that actual user needs and pains are at the centre of the research agenda and act as guidance to the project’s activities. A social science-driven approach supported by social science and ethics experts will guide project research, and a science communication approach will increase the outreach of the outcomes. The project aims to demonstrate through pilots the developed solutions into three relevant sectors of interest: a) finance/loan applications, b) identity verification systems, and c) academic evaluation.

The rapid development and adoption of Artificial Intelligence (AI) and Machine Learning (ML) technologies have brought significant opportunities and challenges. While AI has the potential to revolutionise industries and improve lives, there are growing concerns related to privacy, security, fairness, transparency and the environmental footprint. The Olympics motto "Faster, Higher, Stronger" also applies to recent impressive AI advancements, but now is the time to update it to "Lighter, Clearer, Safer". We propose ACHILLES to build an efficient, compliant, and trustworthy AI ecosystem. At its core is an iterative development cycle inspired by clinical trials encompassing four modules. It begins with human-centric methodologies, followed by data-centric operations, model-centric strategies, and deployment-centric optimisations. It returns to human-centric approaches, focusing on explainability and model monitoring. This iterative cycle aims to enhance AI systems' performance, robustness and efficiency while ensuring they comply with the legal requirements and highest ethical standards. Another innovation is the development of an ML-driven Integrated Development Environment (IDE). The ACHILLES IDE will facilitate seamless integration between the iterative cycle's modules, enabling users to develop efficient, compliant, and trustworthy AI solutions more effectively and responsibly. The project aims to significantly impact European AI development, aligning with the region's guidelines and values. Through innovative techniques and methodologies based on the collaboration of a multidisciplinary team of 16 partners from 10 countries, ACHILLES will foster a strong AI ecosystem that respects privacy, security, and ethical principles across various sectors. By validating the results in real use cases (including healthcare, ID verification, content creation and pharmaceuticals), ACHILLES will showcase its practical applicability and potential for widespread adoption.

SOTERIA aims to drive a paradigm shift on data protection and enable active participation of citizens to their own security, privacy and personal data protection. SOTERIA will develop and test in 3 large-scale real-world use cases, a citizen-driven and citizen-centric, cost-effective, marketable service to enable citizens to control their private personal data easily and securely. Led by an SME, this project will develop, using a user-driven and user-centric design, a revolutionary tool, uniquely combining, in a user-friendly manner, a high-level identification tool with a decentralised secured data storage platform, to enable all citizens, whatever their gender, age or ICT skills, to fully protect and control their personal data while also gaining enhanced awareness on potential privacy risks. SOTERIA solution will be tested and validated through 3 real-world large-scale use-cases, involving 6,500 European citizens, targeting 3 applications which usefulness has been highlighted during COVID-19 pandemic: e-learning, e-voting and e-health. This 3-year transdisciplinary project from both SSH and technology angles, will develop an innovative solution based on: a secured access interface relying on high-level identification, a smart platform processing data to transmit only the minimum personal data required, a secured data storage platform (decentralized architecture) under the full control of the citizen, an educational tool to raise awareness of citizens developed using a citizen-driven and citizen-centric approach. The technologies developed will i) empower citizens to monitor and audit their personal data; ii) restore trust on privacy, security and personal data protection of citizens in digital services; iii) be fully compliant to GDPR regulation and apply strictly the data minimization principle; iv) ensure cybersecurity.