In the domains of aeronautics, automotive, energy, manufacturing and retail, Munich DIH proposes novel solutions to counter the complexity and dependability challenges resulting from distributed accountability, the need for more efficient and intuitive human-CPS interactions as well as the speed and robustness of constantly evolving AI systems.
In the experiment, a proposal for the deployment of the platform for intuitive, accountable AI (PIANAI) for AI-based systems was developed based on clear AI-based innovation projects in these domains, and implemented as an overlay of the DIHIWARE and AI4EU platforms. Moreover, PIANAI services wee be aligned with the AI Manufacturing Testbed in regard to International Data Spaces (IDS), GAIA-X and federated learning. In particular, corresponding interfaces were added and integrate PIANAI services into the testbed. One application could be the implementation of the “clearing house” concept as defined by IDS.
In summary, the experiment offered a technical service as it provides technical means for the definition of verifiable claims regarding the design, deployment, and consumption of an AI service. The claims are defined and supported through tamper-proof facts along the developing process for the AI service under study. Additionally, the claims can be defined post design and development of an AI service, by handling a number of guarantees regarding the service from a Blackbox perspective
The The experiment, comprising a series of smaller experiments, successfully integrated PIANAI as a technological service into the DIH4AI platform. This integration involved four regional AI assets and focused on aligning with IDS and GAIA-X principles, particularly in cloud-edge interoperability through containerized services. Additionally, the experiment contributed to the DIH4AI platform by providing the PIANAI service and a federated learning service, enhancing the platform's innovation and collaboration capabilities. A key aspect of the experiment was its role in operationalizing ethical assessments and certification of AI applications, assessing four AI applications and setting a foundation for engaging more SMEs in future activities. The experiment also laid the groundwork for cross-DIH collaborations, fostering greater cooperation and sharing of innovative practices across Europe. This comprehensive approach not only advanced the capabilities of the PIANAI service but also strengthened the collaborative network of DIHs in the AI sector.