Speaker
Description
Machine Learning provides highly efficient solutions for complex problems. However, the "black-box" or at most grey-box nature of the technology prohibits its use in many critical applications necessitating a throughgoing justification for the correctness of the results delivered.
One rapidly evolving approach is xAI (eXplainable AI) targeting the simultaneous delivery of a result and arguments for its integrity.
An alternative solution is to reuse the rich repertoire of measures collected in the field of fault-tolerant computing. One of the core problems addressed here is to build high-assurance solutions out of not entirely reliable services by using fault-detecting wrappers and redundancy scheme.
The presentation gives an overview of the synergy of AI and FT measures with an outlook to the integration of future xAI based solutions.
