The Future of Explainable AI on the Cloud
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V7I1P140Keywords:
XAI, Cloud, Models, AWS, GCP, AzureAbstract
To guarantee the future of Explainable Artificial Intelligence (XAI) on the cloud, it is necessary to develop transparent, trustworthy, and scalable AI solutions that will connect complex models and human cognition. Since organizations are starting to adopt AI more and more via cloud computing systems such as AWS, Azure, and Google Cloud, it is necessary to incorporate explainability to ensure compliance, ethics, and accountability in decision-making. Cloud-based XAI is an easy-to-use, on-demand model interpretation, visualization, and bias detection tool with guaranteed data privacy and computational efficiency. The research and development of the future will be dedicated to the integration of explainability into automated pipelines to provide real-time insights and user-adaptive explainability based on the user role. As federated learning, edge computing, and regulatory systems emerge, cloud-based XAI will develop into hybrid systems that have a balanced interpretation, performance, and scale. In the long run, it will be a key to building the trust of the users, the responsible use of AI, and open decision-making in the various sectors.
References
[1] Amazon Web Services. (2020, December 8). Detect bias in ML models and explain model behavior with Amazon SageMaker Clarify. https://aws.amazon.com/about-aws/whats-new/2020/12/detect-bias-in-ml-models-and-explain-model-behavior-with-amazon-sagemaker-clarify/ Amazon Web Services, Inc.
[2] IAPP. (2024). Have your cake and eat it, too: Federated learning and edge computing for safe AI innovation. https://iapp.org/news/a/have-your-cake-and-eat-it-too-federated-and-edge-computing-for-safe-ai-innovation IAPP
[3] Amazon Web Services. (2020, December 8). Detect bias in ML models and explain model behavior with Amazon SageMaker Clarify. Retrieved from https://aws.amazon.com/about-aws/whats-new/2020/12/detect-bias-in-ml-models-and-explain-model-behavior-with-amazon-sagemaker-clarify/
[4] Microsoft Azure. (2021). Interpret ML: Model interpretability in Azure Machine Learning. Retrieved from https://learn.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability
[5] Sudjianto, A., Zhang, A., Yang, Z., Su, Y., & Zeng, N. (2023). PiML Toolbox for Interpretable Machine Learning Model Development and Diagnostics. arXiv. Retrieved from https://arxiv.org/abs/2305.04214
[6] 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. Retrieved from https://dl.acm.org/doi/10.1145/2939672.2939778
[7] Explainability in AI Policies: A Critical Review of Communications, Reports, Regulations, and Standards in the EU, US, and UK. (2023). arXiv. Retrieved from https://ar5iv.labs.arxiv.org/html/2304.11218
[8] The Interplay Between Lawfulness and Explainability in the Automated Decision-Making of EU Administration. (2024). Oxford Academic. Retrieved from https://academic.oup.com/book/58128/chapter/479901471
[9] Altukhi, Z. M., Pradhan, S., & Aljohani, N. (2025). A Systematic Literature Review of the Latest Advancements in XAI. Technologies, 13(3), Article 93. https://doi.org/10.3390/technologies13030093
[10] Amazon Web Services. (2020, December 8). Detect bias in ML models and explain model behavior with Amazon SageMaker Clarify. Retrieved from https://aws.amazon.com/about-aws/whats-new/2020/12/detect-bias-in-ml-models-and-explain-model-behavior-with-amazon-sagemaker-clarify/
[11] S. K. Sunkara, "Artificial Intelligence and Machine Learning in Pharma: Revolutionizing Drug Development and Clinical Trials," 2025 12th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida NCR, India, 2025, pp. 1-5, doi: 10.1109/ICRITO66076.2025.11241250.
