  In the future, AI will increasingly find its way into systems that can
potentially cause physical harm to humans. For such safety-critical systems, it
must be demonstrated that their residual risk does not exceed what is
acceptable. This includes, in particular, the AI components that are part of
such systems' safety-related functions. Assurance cases are an intensively
discussed option today for specifying a sound and comprehensive safety argument
to demonstrate a system's safety. In previous work, it has been suggested to
argue safety for AI components by structuring assurance cases based on two
complementary risk acceptance criteria. One of these criteria is used to derive
quantitative targets regarding the AI. The argumentation structures commonly
proposed to show the achievement of such quantitative targets, however, focus
on failure rates from statistical testing. Further important aspects are only
considered in a qualitative manner -- if at all. In contrast, this paper
proposes a more holistic argumentation structure for having achieved the
target, namely a structure that integrates test results with runtime aspects
and the impact of scope compliance and test data quality in a quantitative
manner. We elaborate different argumentation options, present the underlying
mathematical considerations, and discuss resulting implications for their
practical application. Using the proposed argumentation structure might not
only increase the integrity of assurance cases but may also allow claims on
quantitative targets that would not be justifiable otherwise.
