  The European Machinery Directive and related harmonized standards do consider
that software is used to generate safety-relevant behavior of the machinery but
do not consider all kinds of software. In particular, software based on machine
learning (ML) are not considered for the realization of safety-relevant
behavior. This limits the introduction of suitable safety concepts for
autonomous mobile robots and other autonomous machinery, which commonly depend
on ML-based functions. We investigated this issue and the way safety standards
define safety measures to be implemented against software faults. Functional
safety standards use Safety Integrity Levels (SILs) to define which safety
measures shall be implemented. They provide rules for determining the SIL and
rules for selecting safety measures depending on the SIL. In this paper, we
argue that this approach can hardly be adopted with respect to ML and other
kinds of Artificial Intelligence (AI). Instead of simple rules for determining
an SIL and applying related measures against faults, we propose the use of
assurance cases to argue that the individually selected and applied measures
are sufficient in the given case. To get a first rating regarding the
feasibility and usefulness of our proposal, we presented and discussed it in a
workshop with experts from industry, German statutory accident insurance
companies, work safety and standardization commissions, and representatives
from various national, European, and international working groups dealing with
safety and AI. In this paper, we summarize the proposal and the workshop
discussion. Moreover, we check to which extent our proposal is in line with the
European AI Act proposal and current safety standardization initiatives
addressing AI and Autonomous Systems
