A SCALABLE AND ETHICAL FRAMEWORK FOR AI-AUGMENTED ROBOTIC PROCESS AUTOMATION
DOI:
https://doi.org/10.69635/ciai.2025.20Keywords:
Robotic Process Automation, Intelligent Process Automation, Process Mining, Ethical AI, Human-AI CollaborationAbstract
Robotic Process Automation (RPA) has become crucial for digital transformation in businesses. However, traditional RPA systems have issues with scalability, flexibility, and ethical standards. This paper introduces an AI-augmented RPA framework that brings together machine learning, process mining, and built-in governance into one structure. Unlike earlier approaches that handle these aspects separately, this model supports smart decision-making, improves workflows in real-time, and ensures compliant oversight across different industries. The framework is tested using various case studies, stakeholder surveys, and experiments based on simulations. Results show significant improvements in efficiency, fewer errors, better scalability, and more acceptances from the workforce. This study contributes to the conversation on intelligent automation by expanding the literature with a combined ethical-process mining model and offers practical advice for responsibly implementing AI-driven RPA.
References
van der Aalst, W. M. P. (2016). Process mining: Data science in action (2nd ed.). Springer.
Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.
Zhang, L., Li, H., & Chen, Y. (2023). Robotic process automation using process mining: A systematic review. Data & Knowledge Engineering, 144, 102138. https://doi.org/10.1016/j.datak.2022.102138
Bornet, P., Barkin, I., & Wirtz, J. (2021). Intelligent automation: Welcome to the world of hyperautomation. Wiley.
Ali, M., & Kumar, S. (2024). Integration of artificial intelligence and robotic process automation: Proposal for a sustainable model. Applied Sciences, 14(2), 9648. https://doi.org/10.3390/app14029648
van der Aalst, W. M. P. (2025). No AI without PI! Object-centric process mining as the enabler for generative, predictive, and prescriptive artificial intelligence. arXiv. https://arxiv.org/abs/2508.00116
Khayatbashi, S., Zerbino, P., & Reijers, H. A. (2025). AI-enhanced business process automation: A case study in the insurance domain using object-centric process mining. arXiv. https://arxiv.org/abs/2504.17295
Mehdiyev, N., Majlatow, H., & Fettke, P. (2023). Interpretable and explainable machine learning methods for predictive process monitoring: A systematic literature review. arXiv. https://arxiv.org/abs/2312.17584
Palumbo, L., Carneiro, T., & Alves, A. (2024). Objective metrics for ethical AI: A systematic literature review. Journal of Trust Management in AI Systems, 8(1), 541. https://doi.org/10.1007/sXXXXX-024-00541
Suleiman, A., Yusuf, T., & Dada, M. (2024). Process mining enabled cognitive RPA to automate data. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence (ICAART 2024) (pp. 221–230). SCITEPRESS.
Patrício, R., Varela, M. L. R., & Silveira, J. (2024). Integration of artificial intelligence and robotic process automation: Literature review and proposal for a sustainable model. Applied Sciences, 14(21), 9648. https://doi.org/10.3390/app14219648
Papagiannidis, S., Li, F., Bourlakis, M., et al. (2025). Responsible artificial intelligence governance: A review. Information & Management, 62(1), 103767. https://doi.org/10.1016/j.im.2024.103767
Association for the Advancement of Artificial Intelligence. (2022). AI and automation in industry. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 36, pp. 115–124). AAAI Press.
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Copyright (c) 2025 Mohammad Asif Ali (Author)

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