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The European Machinery Directive and related harmonized standards do considerthat software is used to generate safety-relevant behavior of the machinery butdo not consider all kinds of software. In particular, software based on machinelearning (ML) are not considered for the realization of safety-relevantbehavior. This limits the introduction of suitable safety concepts forautonomous mobile robots and other autonomous machinery, which commonly dependon ML-based functions. We investigated this issue and the way safety standardsdefine safety measures to be implemented against software faults. Functionalsafety standards use Safety Integrity Levels (SILs) to define which safetymeasures shall be implemented. They provide rules for determining the SIL andrules for selecting safety measures depending on the SIL. In this paper, weargue that this approach can hardly be adopted with respect to ML and otherkinds of Artificial Intelligence (AI). Instead of simple rules for determiningan SIL and applying related measures against faults, we propose the use ofassurance cases to argue that the individually selected and applied measuresare sufficient in the given case. To get a first rating regarding thefeasibility and usefulness of our proposal, we presented and discussed it in aworkshop with experts from industry, German statutory accident insurancecompanies, work safety and standardization commissions, and representativesfrom various national, European, and international working groups dealing withsafety and AI. In this paper, we summarize the proposal and the workshopdiscussion. Moreover, we check to which extent our proposal is in line with theEuropean AI Act proposal and current safety standardization initiativesaddressing AI and Autonomous Systems

Assurance Cases as Foundation Stone for Auditing AI-enabled and  Autonomous Systems: Workshop Results and Political Recommendations for Action  from the ExamAI Project