The EU Artificial Intelligence Act (AI Act) proposed by the European Commission is a significant legislative effort to regulate AI systems. It is the first legal framework that specifically addresses the risks associated with AI systems, aiming to ensure their trustworthiness and alignment with the values enshrined in the Charter of the Fundamental Rights of the EU and the Union values. Thus, the draft of the AI Act emphasizes the importance of fundamental rights in Europe's AI approach.

The AI Act covers various AI applications, including machine learning, logical, statistical, and knowledge-based approaches. It provides a classification framework based on the purpose and risks posed by AI applications: Prohibited/Unacceptable risk, High-Risk, Limited-Risk, and Minimal/No risk. However, there are concerns about the clarity of the classification criteria mentioned in the AI Act. Some AI systems may fall into multiple classifications, leading to ambiguity. For example, a social robot used in patient treatment could be classified as High-Risk or Limited-Risk. This ambiguity is also observed in classifying AI systems in enterprise functions, where 40{\%} of the classifications remain unclear.

Therefore, these challenges provide an opportunity to improve the classification process of AI systems under the AI Act, facilitating the classification process and accommodating emerging AI technologies. The main research question addressed in this thesis is: \textbf{"To what extent can the process of AI systems classification under the AI Act be improved?"}

The research focuses specifically on AI systems classification. It explores specific provisions of the AI Act, including Prohibited Risk, Classification Rules for High-Risk AI systems, Transparency Obligations, and Annexes II and III.

To achieve the objective of improving the classification accuracy of AI systems based on the AI Act, the study adopts the Design Science Methodology. This methodology involves systematically studying existing AI systems classifications and challenges, extracting themes to develop a framework, and evaluating the framework through feedback from AI experts.

A decision tree is designed as the proposed framework. It is evaluated on 16 respondents from two different backgrounds: legal and non-legal. In order to obtain comprehensive insights, the evaluation is designed to incorporate an experiment where respondents are tasked to classify AI systems to the risk level with the AI Act only. Then in the second experiment, they have to classify AI systems using the proposed decision tree framework. It is important to note that the study acknowledges the possibility of overestimating or underestimating respondents' ability to classify AI systems due to their diverse backgrounds and levels of understanding of the AI Act. Furthermore, a semi-structured interview is conducted to strengthen the analysis.

Based on the evaluation, the decision tree's performance revealed higher accuracy than the classification approach without the decision tree. However, the overall accuracy remained low, indicating room for improvement. Challenges identified include the need for additional context and understanding of terms, definitions, and examples in the decision tree and the potential for misclassification due to vague definitions and assumptions. Respondents also expressed the need for more detailed information about AI system use cases to improve classification accuracy.

The decision tree's performance varied between obvious and non-obvious use cases, with non-obvious cases presenting challenges in accurate classification. The accuracy for obvious cases was higher, highlighting the difficulty of distinguishing between High-Risk and Unacceptable Risk categories. Lack of clarity in terms and definitions and limited contextual information contributed to the challenges faced in classifying non-obvious cases.

Legal experts demonstrated higher accuracy than non-legal respondents, indicating familiarity with legal terminology and the AI Act. However, legal and non-legal respondents encountered difficulties classifying non-obvious cases, emphasizing the need for clearer frameworks and tools to enhance clarity and streamline the classification process. Greater clarity in the AI Act and an interdisciplinary approach were recommended to address these challenges and facilitate understanding of the risks associated with AI systems.

Based on the analysis, several areas for improving AI systems classification under the AI Act have been identified. The current classification process faces challenges related to ambiguities in definitions, lack of contextual information, and difficulties in distinguishing between different risk levels.

To address these challenges and enhance the classification process, it is recommended to introduce clearer guidelines and refine the decision tree used for classification. The decision tree should incorporate additional criteria and features that provide more clarity and context. It is important to consider biases, subjective interpretations, clarity, and the dynamic nature of AI technologies in these improvements.

The study has certain limitations. The small sample size of respondents may impact the generalizability of the findings. The number of participants might not be representative of the entire population. Additionally, the limited number of use cases utilized in the research may limit the comprehensiveness of the classification framework. The study is based on the latest amendment of a policy proposal, and there is a potential for changes in the regulation's details, which may affect the effectiveness of the results. Finally, potential biases may exist in the development of the research, such as in making the decision tree and selecting the use cases.

Future research should explore the continuity of the decision tree's performance over time and its evaluation. There should be more research on non-obvious cases in specific domains or industries. It is crucial to focus on potential issues in classifying certain risk levels in the AI Act that hinder classification accuracy. Understanding the differences between legal and non-legal perspectives on the AI Act is also important to establish standardized understanding among stakeholders. Additionally, conducting quantitative research with larger and more diverse respondents from industrial backgrounds can further evaluate the proposed framework.