Platform-as-a-service for accountable evidential transactions
In the domains of aeronautics, automotive, energy, manufacturing and retail, Munich Innovation Hub for Applied AI 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 willbe elaborated (WP5), based on clear AI-based innovation projects in these domains, and implemented as an overlay of the DIHIWARE and AI4EU platforms. Moreover, PIANAI services will be aligned with the AI Manufacturing Testbed provided by TNO (WP6), in regardsto International Data Spaces (IDS), GAIA-X and federated learning. In particular, we will provide corresponding interfaces and integrate PIANAI services into the testbed. One particular application could be the implementation of the “clearing house” concept as defined by IDS.The experiment offers a technical service. 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 devops process for the AI service under study. Additionally, the claims can be defined post design and development of a AI service, by handling a number of guarantees regarding the service from a blackbox perspective.
AI offers promising capabilities to improve various data-intensive processes. At the same time, adequate training of models using traditional learning techniques requires the collection and storage of enough training data in a central place. Moreover, AI often requires bundles of models and services provided by different entities in a decentralized and distributed manner. Unfortunately, due to business, ethical, legislative and jurisdictional constraints, data in a central place is scarce and training a model becomes unfeasible. Additionally, sharing data between the services of different entities underlies similar restrictions.Against this backdrop, PIANAI explores novel approaches and techniques such as distributed ledger technologies (DLT) and combines them with established argumentation and knowledge management techniques (e.g. assurance, semantic web/ ontologies, formal verification) towards designing and implementing accountable AI services. To showcase its benefits in terms of providing a trust layer for various AI services, our experiments with PIANAI focus integrating bleeding-edge AI approaches such as federated learning, a technique to collaboratively train models without transferring data to a centralized location. With federated learning, each entity keeps its data private, and new applications that previously were impossible now can be a reality.
The experiment –as a bundle of “small” experiments” –will contribute with PIANAI as a technology service to the DIH4AI platform and benefit from the latter as follows. First, in regardsto the DIH4AI platform as an on-demand platforms for innovative AI solutions “made in Europe”, the experiment will integrate #4 regional AI Assets. Second, regarding the envisioned regional-European platforms interoperability framework, the experiment focuses on analyzing and designing functionalities along IDS and GAIA-Xprinciples towards the integration of the regional platform PIANAI integrated. This effort includes the topic of cloud-edge interoperability in terms of containerized services provided. Third, the experiment will contribute to the objective of creating Innovation and Collaboration Platform for DIHs in terms of providing #2 Assets to be accessed by the DIH4AI platform: the PIANAI service as well as a federated learning service. Fourth, the experiment will serve as a basis for operationalizing AI Applications Ethical Assessment and Certification by assessing #4 AI applications and providing verifiable claims for their auditing. Fifth, the Intra-DIH Regional experiment will involve #2 SMEs and potentially engage up to #4 in follow-up activities. Finally, our experiment will include cross-DIH collaborative activities, where a joint provision and development will take place together with TNO and their testbed.