Many different actors come together in value chains. In order for the various AI systems of these actors to be able to work together automatically, a data reference model is needed to exchange data securely, reliably, flexibly and compatibly. Standards for data reference models from different areas create the basis for a comprehensive data exchange and thus ensure the interoperability of AI systems worldwide.

AI systems are essentially IT systems - for the latter there are already many standards and specifications from a wide range of application areas. To enable a uniform approach to the IT security of AI applications, an overarching "umbrella standard" that bundles existing standards and test procedures for IT systems and supplements them with AI aspects would be expedient. This basic security standard can then be supplemented by subordinate standards on other topics.

When self-learning AI systems decide about people, their possessions, or access to scarce resources, unplanned problems in AI can endanger individual fundamental rights or democratic values. So that AI systems in ethically uncritical fields of application can still be freely developed, an initial criticality test should be designed through standards and specifications - this can quickly and legally clarify whether an AI system can even trigger such conflicts.

So far, there is a lack of reliable quality criteria and test procedures for AI systems - this endangers the economic growth and competitiveness of this future technology. A national implementation programme "Trusted AI" is required, which forms the foundation for reproducible and standardized test procedures.  These are used to test the characteristics of AI systems such as reliability, robustness, performance and functional safety and to make statements about trustworthiness.  Standards and specifications describe requirements for these properties and thus form the basis for the certification and conformity assessment of AI systems. With such an initiative, Germany has the opportunity to develop a certification programme that will be the first of its kind in the world and will be internationally recognized.

AI research and the industrial development and application of AI systems are highly dynamic. Already today there are many applications in the different fields of AI. Standardization needs for AI applications ready for industrial use can be derived from application-typical and industry-relevant use cases. In order to shape standards and specifications, it is important to integrate mutual impulses from research, industry, society and regulation. At the centre of this approach, the developed standards should be tested and further developed on the basis of use cases. In this way, application-specific requirements can be identified at an early stage and marketable AI standards realized.