The Artificial Intelligence Act of the European Union mandates that providers and deployers of high-risk AI systems establish a quality management system (QMS). Among other criteria, a QMS shall help to i) identify, analyze, evaluate, and mitigate risks, ii) ensure evidence of compliance with training, validation, and testing data, and iii) verify and document the AI system design and quality. Current research mainly addresses conceptual considerations and framework designs for AI risk assessment and auditing processes. However, it often overlooks practical tools that actively involve and support humans in checking and documenting high-risk or general-purpose AI systems. This paper addresses this gap by proposing requirements derived from legal regulations and a generic design and architecture of a QMS for AI systems verification and documentation. A first version of a prototype QMS is implemented, integrating LLMs as examples of AI systems and focusing on an integrated risk management sub-service. The prototype is evaluated on i) a user story-based qualitative requirements assessment using potential stakeholder scenarios and ii) a technical assessment of the required GPU storage and performance.

Classification as a supervised learning concept is an important content in machine learning. It aims at categorizing a set of data into classes. There are several commonly-used classification methods nowadays such as k-nearest neighbors, random forest, and support vector machine. Each of them has its own pros and cons, and none of them is invincible for all kinds of problems. In this thesis, we focus on Quadratic Multiform Separation (QMS), a classification method recently proposed by Michael Fan et al. (2019). Its fresh concept, rich mathematical structure, and innovative definition of loss function set it apart from the existing classification methods. Inspired by QMS, we propose utilizing a gradient-based optimization method, Adam, to obtain a classifier that minimizes the QMS-specific loss function. In addition, we provide suggestions regarding model tuning through explorations of the relationships between hyperparameters and accuracies. Our empirical result shows that QMS performs as good as most classification methods in terms of accuracy. Its superior performance is almost comparable to those of gradient boosting algorithms that win massive machine learning competitions.