Explainability in Process Mining: A Framework for Improved Decision-Making

Explainability in Process Mining: A Framework for Improved Decision-Making

연구 분야: Artificial Intelligence

학회: AIES '23: Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society

This research project aims to develop and validate explanatory facilities to enhance information reception of process mining solutions, which could inform and be translated to other business intelligence platforms. Process mining, a nascent field for analyzing event data stored in information systems, faces challenges in adoption, engagement, and comprehensive explainability frameworks. The research problem lies in the difficulties organizations face when understanding the return on investment and integration requirements associated with process mining operationalization. Furthermore, users often struggle to comprehend the elaboration and representation of process outputs. This issue is compounded by the limited application of Explainable AI (XAI) in process mining, which so far has been predominantly focused on prediction and monitoring activities without a holistic view of explainability trade-offs.