mlsanityData to 14 Oct 2026

Design of a Quality Management System based on the EU Artificial Intelligence Act

arXiv 2408.04689 ↗First posted 8 Aug 2024cs.SE cs.AI cs.CYcited by 0

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Abstract (arXiv)

The EU AI Act mandates that providers and deployers of high-risk AI systems establish a quality management system (QMS). Among other criteria, a QMS shall help verify and document the AI system design and quality and monitor the proper implementation of all high-risk AI system requirements. Current research rarely explores practical solutions for implementing the EU AI Act. Instead, it tends to focus on theoretical concepts. As a result, more attention must be paid to tools that help humans actively check and document AI systems and orchestrate the implementation of all high-risk AI system requirements. Therefore, this paper introduces a new design concept and prototype for a QMS as a microservice Software as a Service web application. It connects directly to the AI system for verification and documentation and enables the orchestration and integration of various sub-services, which can be individually designed, each tailored to specific high-risk AI system requirements. The first version of the prototype connects to the Phi-3-mini-128k-instruct LLM as an example of an AI system and integrates a risk management system and a data management system. The prototype is evaluated through a qualitative assessment of the implemented requirements, a GPU memory and performance analysis, and an evaluation with IT, AI, and legal experts.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 51 references that are in the map. First posted on arXiv 8 Aug 2024.

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References · 16

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4 more
  1. 2023Towards Concrete and Connected AI Risk Assessment (C2AIRA): A Systematic Mapping Studycited by 9
  2. 2021A Survey on Methods and Metrics for the Assessment of Explainability under the Proposed AI Actcited by 8
  3. 2021Trustworthy Artificial Intelligence and Process Mining: Challenges and Opportunitiescited by 4
  4. 2022An Audit Framework for Technical Assessment of Binary Classifierscited by 1

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