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

arXiv: https://arxiv.org/abs/2408.04689 · HTML: https://mlsanity.com/paper/2408.04689/

## 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.

## Builds on (most cited first, 16 of 16)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://mlsanity.com/paper/2404.14219/) (2024)
- [Understanding Neural Networks through Representation Erasure](https://mlsanity.com/paper/1612.08220/) (2016)
- [LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples](https://mlsanity.com/paper/2310.01469/) (2023)
- [A Systematic Assessment of Syntactic Generalization in Neural Language Models](https://mlsanity.com/paper/2005.03692/) (2020)
- [Auditing large language models: a three-layered approach](https://mlsanity.com/paper/2302.08500/) (2023)
- [Can Large Language Models Explain Themselves? A Study of LLM-Generated Self-Explanations](https://mlsanity.com/paper/2310.11207/) (2023)
- [Post Hoc Explanations of Language Models Can Improve Language Models](https://mlsanity.com/paper/2305.11426/) (2023)
- [Benchmarking LLM powered Chatbots: Methods and Metrics](https://mlsanity.com/paper/2308.04624/) (2023)
- [Towards Concrete and Connected AI Risk Assessment (C2AIRA): A Systematic Mapping Study](https://mlsanity.com/paper/2301.11616/) (2023)
- [A Survey on Methods and Metrics for the Assessment of Explainability under the Proposed AI Act](https://mlsanity.com/paper/2110.11168/) (2021)
- [Trustworthy Artificial Intelligence and Process Mining: Challenges and Opportunities](https://mlsanity.com/paper/2110.02707/) (2021)
- [An Audit Framework for Technical Assessment of Binary Classifiers](https://mlsanity.com/paper/2211.09500/) (2022)

## 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.