Securing and expanding the competitiveness of German small and medium-sized enterprises (SMEs) requires a compelling concept for the cross-industry adoption of artificial intelligence (AI). With AI as a scalable service ("AI as a Service"), SMEs can independently generate added value through AI even without in-depth AI expertise and with limited resources. However, the lack of explainability of AI systems hinders the widespread dissemination of "AI as a Service."
Research in the field of Explainable Artificial Intelligence (XAI) offers solutions to this problem. Through automatically generated explanations of AI results, users can understand the decisions suggested by AI systems and learn how to use AI responsibly ("AI literacy"). Today, application of XAI methods requires a high level of AI expertise and specific development effort.
The aim of the XAIaaS project, funded by the Federal Ministry of Education and Research in the KI4KMU program, is to explore the use of XAI methods in the spirit of "XAI as a service" to increase AI adoption and close AI literacy gaps in SMEs. Based on a no-code platform, "XAI as a service" approaches for representative and transferable use cases are designed, implemented, and evaluated for effectiveness.
Supported by the Federal Ministry of Education and Research in the KI4KMU program.
Are you an SME (fewer than 250 employees and less than 50 million € annual revenue) with headquarters in Germany, and would you like early access to "XAI as a Service" technology? Get in touch!
New BMBF project XAI as a Service has started
The project results are expected to provide high scientific and economic benefits for German SMEs.
An Explainable AI taxonomy developed for SMEs from various industries, in combination with realized prototypes as demonstrators in two transferable use cases (core competencies and peripheral competencies), allows for cross-industry transfer of results.
Better User Experience with Explainable AI
A structured approach for the development of explanation components has proven successful in numerous projects in research and practice.
In four consecutive phases, we first capture the target group and application context, then identify suitable XAI methods, which we then test with a prototype and finally develop into an application-ready component.
