### Thematic Analysis of the Provided Text

#### 1. **Identification and Listing of Key Codes**
The following key codes (recurring concepts or ideas) have been identified in the text:

- **AI Ethics Compliance**: Emphasis on adhering to ethical standards, specifically the EU AI Act.
- **Bias Mitigation**: Incorporating methods to detect and mitigate bias in AI predictions.
- **Error Handling**: Ensuring robust handling of errors through logging and exceptions.
- **User Feedback Integration**: Incorporating user feedback into the system for ongoing improvement.
- **Transparency and Accountability**: Maintaining transparency in the AI system's operations and decision-making processes.
- **Statistical Analysis**: Use of statistical methods, such as Chi-Square tests, for bias evaluation.
- **Continuous Improvement**: Iterative refinement of the AI model based on feedback and performance assessments.
- **Modular Code Design**: Focus on modular and clean coding practices to improve maintainability.
- **Confidence Scores**: Returning confidence levels alongside predictions to inform users about prediction reliability.

#### 2. **Grouping of Codes into Broader Themes**
The identified codes can be grouped into the following broader themes:

- **Ethical AI Development**
  - AI Ethics Compliance
  - Bias Mitigation
  - Transparency and Accountability

- **Robust System Design**
  - Error Handling
  - Modular Code Design

- **User-Centric AI Improvement**
  - User Feedback Integration
  - Continuous Improvement

- **Data-Driven Evaluation**
  - Statistical Analysis
  - Confidence Scores

#### 3. **Explanation of the Relationship Between Codes and Themes**
- **Ethical AI Development**: The codes under this theme highlight the importance of aligning AI development with ethical standards and regulatory requirements. The AI Ethics Compliance code underscores the commitment to adhering to the EU AI Act, while Bias Mitigation ensures that the system actively works against potential biases. Transparency and Accountability emphasize the need for clear and open operations, ensuring that stakeholders understand how decisions are made within the AI system.

- **Robust System Design**: This theme is centered around creating a reliable and maintainable AI system. Error Handling focuses on managing unexpected situations effectively to prevent system failures. Modular Code Design ensures that the system remains organized, making it easier to maintain and update.

- **User-Centric AI Improvement**: The codes here reflect a commitment to refining the AI model based on real-world interactions and feedback. User Feedback Integration is crucial for adapting the model to user needs and preferences, while Continuous Improvement highlights the iterative nature of AI development, where the model evolves through regular updates based on user feedback and performance metrics.

- **Data-Driven Evaluation**: This theme emphasizes the importance of using data and statistical methods to inform decisions within the AI system. Statistical Analysis involves the application of tests like the Chi-Square to evaluate the system's performance and detect biases, while Confidence Scores provide users with insight into the reliability of predictions, enhancing the system's trustworthiness.

#### 4. **Summary of Identified Themes and Their Significance**
The thematic analysis reveals a comprehensive approach to developing a responsible AI system. The **Ethical AI Development** theme highlights the project's dedication to creating an AI system that not only performs well but also adheres to ethical standards, particularly the EU AI Act. **Robust System Design** ensures that the AI system is resilient and maintainable, capable of handling errors gracefully while being organized for future development.

**User-Centric AI Improvement** signifies the importance of continuously refining the AI model based on user interactions and feedback, ensuring that the system remains relevant and effective in real-world scenarios. Finally, the **Data-Driven Evaluation** theme underscores the reliance on empirical methods and statistical analysis to maintain and improve the system's fairness and accuracy.

These themes collectively demonstrate a balanced approach to AI development, where ethical considerations, user feedback, and rigorous evaluation drive the ongoing improvement of the system, ensuring it remains effective, fair, and trustworthy.