Artificial intelligence can be used to realise new types of protective devices and assistance systems, so their importance for occupational safety and health is continuously increasing. However, established risk mitigation measures in software development are only partially suitable for applications in AI systems, which only create new sources of risk. Risk management for systems that for systems using AI must therefore be adapted to the new problems. This work objects to contribute hereto by identifying relevant sources of risk for AI systems. For this purpose, the differences between AI systems, especially those based on modern machine learning methods, and classical software were analysed, and the current research fields of trustworthy AI were evaluated. On this basis, a taxonomy could be created that provides an overview of various AI-specific sources of risk. These new sources of risk should be taken into account in the overall risk assessment of a system based on AI technologies, examined for their criticality and managed accordingly at an early stage to prevent a later system failure.

We demonstrate the simultaneous mapping of haemoglobin oxygenation and blood flow changes of human skin and the exposed cortex in rats by broad-band reflectometry and laser speckle contrast analysis (LASCA). A hard- and software set-up is realized with a high temporal resolution achieved by techniques of multi- and manycore computing. Spatial and temporal resolved changes of haemoglobin oxygenation and blood flow in rats are observed in response to middle cerebral artery occlusion and cortical spreading depression.