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Sources of Risk of AI Systems

Author

Listed:
  • André Steimers

    (Institute for Occupational Safety and Health of the German Social Accident Health Insurance (IFA), 53757 Sankt Augustin, Germany)

  • Moritz Schneider

    (Institute for Occupational Safety and Health of the German Social Accident Health Insurance (IFA), 53757 Sankt Augustin, Germany)

Abstract

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.

Suggested Citation

  • André Steimers & Moritz Schneider, 2022. "Sources of Risk of AI Systems," IJERPH, MDPI, vol. 19(6), pages 1-32, March.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:6:p:3641-:d:774615
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    References listed on IDEAS

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    1. Sebastian Bach & Alexander Binder & Grégoire Montavon & Frederick Klauschen & Klaus-Robert Müller & Wojciech Samek, 2015. "On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-46, July.
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