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Machine learning has made remarkable advancements, but confidently utilisinglearning-enabled components in safety-critical domains still poses challenges.Among the challenges, it is known that a rigorous, yet practical, way ofachieving safety guarantees is one of the most prominent. In this paper, wefirst discuss the engineering and research challenges associated with thedesign and verification of such systems. Then, based on the observation thatexisting works cannot actually achieve provable guarantees, we promote atwo-step verification method for the ultimate achievement of provablestatistical guarantees.

What, Indeed, is an Achievable Provable Guarantee for Learning-Enabled Safety Critical Systems