To design a GUI that provides transparency into AI decision-making processes:

1. Explainable AI (XAI) integration: Incorporate XAI techniques to provide insights into AI decisions.

2. Clear labeling: Label AI-driven features and decisions clearly.

3. Decision trails: Display the sequence of events leading to an AI decision.

4. Model interpretability: Use techniques like feature attribution to explain model behavior.

5. Confidence indicators: Show the confidence level of AI predictions or decisions.

6. What-if scenarios: Allow users to explore hypothetical scenarios to understand AI decision-making.

7. Model-agnostic explanations: Provide explanations that are independent of the AI model used.

8. Interactive visualizations: Use interactive visualizations to help users understand complex AI decisions.

9. Transparency into data usage: Explain how data is used to train and validate AI models.

10. User control: Allow users to adjust AI parameters or provide feedback to improve transparency.

11. Documentation and guides: Provide detailed documentation and guides to help users understand AI decision-making.

12. Regular updates and maintenance: Regularly update and maintain the GUI to ensure transparency and accuracy.

By incorporating these elements, you can create a GUI that provides transparency into AI decision-making processes, building trust and understanding with users.