Trustworthy Multimodal Creator Intelligence: Integrating Explainable LLM Agents, Federated Analytics, and Differential Privacy for Social E-Commerce Growth
Keywords:
Creator intelligence, multimodal AI, explainable LLM agents, federated analytics, differential privacy, social e-commerce, trustworthiness, adaptive privacy budgets, cross-device attributionAbstract
The convergence of multimodal content generation, large language models, and social commerce platforms has given rise to Creator Intelligence, a new paradigm in which individual creators leverage AI tools to produce personalized marketing materials, interact with audiences, and drive transactional outcomes. However, the widespread deployment of such systems introduces profound challenges in trustworthiness, data privacy, and accountability. This paper proposes a comprehensive architectural framework that integrates explainable LLM agents, federated analytics, and differential privacy to build a trustworthy multimodal creator intelligence ecosystem for social e-commerce. The framework addresses key structural trade-offs between utility, privacy, scalability, and interpretability. We examine the socio-technical implications of deploying federated attribution mechanisms that preserve user-level privacy while enabling cross-device measurement and targeted advertising. Differential privacy budgets are adaptively allocated based on content sensitivity and creator reputation, ensuring that individual user contributions are protected without compromising aggregate analytics. The role of explainable LLM agents is articulated through a layered explanation hierarchy that spans model reasoning, content provenance, and decision traceability. We also discuss governance models, fairness constraints, and policy recommendations for sustainable operation. Through case illustrations drawn from real-world deployment scenarios, we highlight the robustness and adaptability of the proposed system. The paper concludes with a forward-looking discussion on emerging regulatory frameworks and the need for continuous auditing mechanisms in multimodal AI-driven commerce. This work contributes both a theoretical foundation and a practical blueprint for building trustworthy AI systems at the intersection of creativity, data protection, and social commerce growth.
References
1. Linden, G., Smith, B., & York, J. (2003). Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, 7(1), 76–80.
2. Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., ... & Sutskever, I. (2021). Zero-shot text-to-image generation. Proceedings of the International Conference on Machine Learning, 8821–8831.
3. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why should I trust you?”: Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–1144.
4. McMahan, B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 1273–1282.
5. Cormode, G., Jha, S., Kulkarni, T., Li, N., Srivastava, D., & Wang, T. (2018). Privacy at scale: Local differential privacy in practice. Proceedings of the 2018 International Conference on Management of Data, 2037–2039.
6. Dwork, C., McSherry, F., Nissim, K., & Smith, A. (2006). Calibrating noise to sensitivity in private data analysis. Theory of Cryptography Conference, 265–284.
7. Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., ... & Seth, K. (2017). Practical secure aggregation for privacy-preserving machine learning. Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, 1175–1191.
8. Bhowmick, A., Duchi, J., Freudiger, J., Kapoor, G., & Rogers, R. (2019). Protection against reconstruction and its applications in private federated learning. arXiv preprint arXiv:1812.00984.
9. Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3), 50–60.
10. Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., ... & Ramage, D. (2019). Towards federated learning at scale: System design. Proceedings of Machine Learning and Systems, 1, 374–388.
11. Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019). Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology, 10(2), 1–19.
12. Shi, C., Li, S., Guo, S., Xie, S., Wu, W., Dou, J., ... & Chua, T. S. (2025). Where Culture Fades: Revealing the Cultural Gap in Text-to-Image Generation. arXiv preprint arXiv:2511.17282.
13. Zhou, D. (2026). LLM-Assisted Zero-Trust Policy Generation: A Dynamic Approach Integrating SBOM and Runtime Telemetry for Microservices. American Journal Of Big Data, 7(1), 212-228.
14. Erlingsson, Ú., Pihur, V., & Korolova, A. (2014). RAPPOR: Randomized aggregatable privacy-preserving ordinal response. Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, 1054–1067.
15. Jain, S., & Wallace, B. C. (2019). Attention is not explanation. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 3543–3556.
16. Zyskind, G., Nathan, O., & Pentland, A. (2015). Decentralizing privacy: Using blockchain to protect personal data. 2015 IEEE Security and Privacy Workshops, 180–184.
17. Barocas, S., & Selbst, A. D. (2016). Big data's disparate impact. California Law Review, 104(3), 671–732.
18. Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., ... & Zhao, S. (2021). Advances and open problems in federated learning. Foundations and Trends in Machine Learning, 14(1–2), 1–210.
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