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Summary of Navigating the Eu Ai Act: a Methodological Approach to Compliance For Safety-critical Products, by J. Kelly et al.


by J. Kelly, S. Zafar, L. Heidemann, J. Zacchi, D. Espinoza, N. Mata

First submitted to arxiv on: 25 Mar 2024

Categories

  • Main: Artificial Intelligence (cs.AI)
  • Secondary: None

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GrooveSquid.com Paper Summaries

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Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
The paper presents a methodology for interpreting the EU AI Act requirements for high-risk AI systems by leveraging product quality models. The authors propose an extended product quality model for AI systems, incorporating attributes relevant to the Act not covered by current quality models. They map the Act requirements to relevant quality attributes with the goal of refining them into measurable characteristics. A contract-based approach is proposed to derive technical requirements at the stakeholder level, facilitating the development and assessment of AI systems that adhere to established quality standards and comply with regulatory requirements. The methodology is demonstrated on an exemplary automotive supply chain use case, where several stakeholders interact to achieve EU AI Act compliance.
Low GrooveSquid.com (original content) Low Difficulty Summary
The paper helps create a safe and trustworthy way for AI systems to be used in high-risk situations like self-driving cars. It shows how to take the European Union’s AI guidelines and turn them into specific requirements that companies can follow to make sure their AI products are good enough. The authors use a special kind of model to combine different qualities of an AI system, like its safety and reliability, and then use those qualities to create rules for what the AI should be able to do. They test this idea on a real-world example of an automotive supply chain.

Keywords

» Artificial intelligence