Your expanded exploration into the legal standards for transparency and explainability in AI decision-making is both comprehensive and insightful. Here’s how each of your areas of focus, along with the additional considerations, can be integrated to create a robust research framework:

1. **Comparative Analysis of Existing Regulations**:
   - Develop a timeline that traces key regulatory milestones in AI transparency across different jurisdictions. This timeline can provide a visual representation of progress and highlight pivotal changes.
   - Delve into case studies where regulatory bodies have acted against companies for non-compliance, analyzing the effectiveness of enforcement mechanisms and the implications of these actions on industry practices.

2. **Stakeholder Perspectives**:
   - Utilize a mixed-methods approach to gather diverse stakeholder insights, ensuring both breadth and depth in understanding different viewpoints.
   - Integrate cultural analysis to examine how cultural contexts influence stakeholder perceptions and expectations regarding AI transparency and accountability.

3. **Public Trust and Accountability**:
   - Conduct demographic analyses to identify trends in public trust across different population segments, paying attention to how these demographics interact with AI technologies.
   - Implement sentiment analysis of social media platforms to capture real-time public perceptions and reactions to AI-driven decisions, offering an additional layer of understanding to traditional survey data.

4. **Technical Challenges**:
   - Evaluate the feasibility and effectiveness of using tools like LIME or SHAP across various AI applications, and investigate the possibility of developing standardized guidelines for their implementation.
   - Discuss the inherent trade-offs between model performance and explainability, potentially leading to recommendations on balancing these factors based on application context.

5. **Interdisciplinary Approaches**:
   - Document case studies where interdisciplinary collaboration has successfully resolved AI transparency challenges, highlighting best practices and lessons learned.
   - Compare different ethical frameworks and their influence on creating regulatory policies, facilitating a dialogue on achieving consensus in developing unified standards.

6. **Future Directions**:
   - Examine the impact of international organizations in harmonizing global standards for AI transparency, potentially advocating for more coherent international policy frameworks.
   - Explore how blockchain and other decentralized technologies could offer new pathways for ensuring greater accountability and traceability in AI systems.

By weaving these considerations into your research, you'll be well-positioned to contribute meaningfully to the discourse surrounding AI transparency and regulation. This holistic approach not only aids in understanding the current landscape but also equips stakeholders with actionable insights for future development. If there are particular areas within these categories that deserve a deeper focus, such as specific technologies or legal frameworks, please indicate those to tailor your examination further.