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.

HOMER is aiming at the development of non-intrusive experimental flow diagnostic and data assimilation methods to expand capabilities from the aerodynamic analysis to the investigations of fluid-structure-interactions (FSI) in wind tunnels and other test facilities. The objective of the project is to develop an unattained combined diagnostic approach with simultaneous optical measurements of fluid and structure. When this is achieved, the measurements can be treated invoking the relation between the balancing forces (inertia-, elastic- and aerodynamic forces) interacting (non-linearly) within the s.c. Collar Triangle (FI + FE + FA = 0). The research focuses on the application and further development of time-resolved volumetric (4D) flow field measurements that enable determining the fluid flow pressure. 3D PIV and Lagrangian Particle Tracking (LPT) along with Digital Image Correlation (DIC) are tailored to determine the position and dynamics of fluid and surface motion and deformations. Pressure Sensitive Paint (PSP) methods will be employed simultaneously with DIC and PIV/LPT to obtain the surface pressure at transonic flow velocities together with the model deformation. The project realizes experiments that support the validation needs of MDO tool developments, enhance the physical knowledge about Fluid-Structure-Interaction phenomena and range from the assessment of the method (turbulent flow over a deforming surface) to relevant problems in aeronautics (transonic buffeting) and flapping flight mechanics.