Standards for AI, Safety and Automation

In the mechanical engineering standards landscape, application-specific standards are generally superior to generic or general standards. However, in the absence of an established application-specific solution, process, or methodology, these specific standards reference higher standards. In the area of artificial intelligence (AI) and machine learning (ML), this situation becomes even more complex. For example, some standards include aspects of AI and ML, while others do not.

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Challenge: Implementation and Compliance with Industry Standards

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One problem is that not all relevant security standards take existing AI/ML solutions (or applications) into account. Verification of conformance is therefore complex and requires a customized approach. In addition, application-specific norms and standards related to AI/ML often refer to generic norms that specify the objectives to be achieved, but do not provide specific ways or methods to achieve them (using AI). This leads to another challenge in implementing and complying with security-related standards.

Whitepaper: The European Union Artificial Intelligence Act

In this whitepaper, we demonstrate how our methodology can be applied across three industry-specific usecases:

  • Automotive: Automated Parking System
  • Industrial Automation: Quality Inspection Cobot
  • Healthcare: Brain-Computer Interface

The flexibility of the methodology allows for extensions and modifications depending on the use-case at hand. For each use-case, we present the approach for verifying a different sub-quality attribute for a particular stakeholder in the AI value chain.

Download Whitepaper: The European Union Artificial Intelligence Act

Our other core competencies

In addition to Standards for AI, Safety and Automation, we also focus on the following topics:

Person and Object Detection

Robust AI: Uncertainty Estimation


Industrial Sensors

The automation of production requires reliable systems for the real-time monitoring and control of processes. Visit Industrial Sensors to learn more about our focus areas and the various projects Fraunhofer IKS is working on.

​FAST -Feedback-guided Automation of Sub-tasks

​Modular Concept Learning