Outline of an Independent Systematic Blackbox Test for ML-based Systems

ML-based systems are used today in a wide range of areas, and increasingly also in safety-critical domains. Their range of application is growing exponentially. At the same time, more and more experts are warning of the uncertainties and risks associated with the uncontrolled and overly rapid development of AI systems Bengio et al. [22.03.2023]. In general, there is a growing need to provide methods and procedures for testing functioning and quality characteristics of these systems. Various methods currently exist to test and verify ML-based systems, be it formal verification, simulation approaches or classical testing Albarghouthi, Jackson et al., Vasu Singh et al., or new analysis methods in the context of XAI Hoyer et al., Guidotti et al.. The methods aim for providing evidence on the robustness and trustworthiness of the ML models or ML-based system (ML - Machine Learning). Similar to the traditional development of complex software systems, testing has also proven to be the most effective method for proving quality and gaining trust in ML.