Explainable Artificial Intelligence for Process Mining: A General Overview and Application of a Novel Local Explanation Approach for Predictive Process Monitoring

Abstract

The contemporary process-aware information systems possess the capabilities to record the activities generated during the process execution. To leverage these process specific fine-granular data, process mining has recently emerged as a promising research discipline. As an important branch of process mining, predictive business process management, pursues the objective to generate forward-looking, predictive insights to shape business processes. In this study, we propose a conceptual framework sought to establish and promote understanding of decision-making environment, underlying business processes and nature of the user characteristics for developing explainable business process prediction solutions. Consequently, with regard to the theoretical and practical implications of the framework, this study proposes a novel local post-hoc explanation approach for a deep learning classifier that is expected to facilitate the domain experts in justifying the model decisions. In contrary to alternative popular perturbation-based local explanation approaches, this study defines the local regions from the validation dataset by using the intermediate latent space representations learned by the deep neural networks. To validate the applicability of the proposed explanation method, the real-life process log data delivered by the Volvo IT Belgium's incident management system are used.The adopted deep learning classifier achieves a good performance with the Area Under the ROC Curve of 0.94. The generated local explanations are also visualized and presented with relevant evaluation measures that are expected to increase the users' trust in the black-box-model.

Manuscript submitted (10.07.2020) to the edited volume "Interpretable Artificial Intelligence: A perspective of Granular Computing" (published by Springer)

References in corpus (9)

• Towards A Rigorous Science of Interpretable Machine Learning
• Striving for Simplicity: The All Convolutional Net
• Explaining Explanations in AI
• SmoothGrad: removing noise by adding noise
• A systematic review and taxonomy of explanations in decision support and recommender systems
• Grad-CAM: Why did you say that?
• Distilling a Neural Network Into a Soft Decision Tree
• Decay Replay Mining to Predict Next Process Events
• Stakeholders in Explainable AI

Cited by in corpus (5)

• Building Interpretable Models for Business Process Prediction using Shared and Specialised Attention Mechanisms
• Predictive Compliance Monitoring in Process-Aware Information Systems: State of the Art, Functionalities, Research Directions
• Beyond Model Interpretability: Socio-Structural Explanations in Machine Learning
• Local Post-Hoc Explanations for Predictive Process Monitoring in Manufacturing
• Identifying contributors to supply chain outcomes in a multi-echelon setting: a decentralised approach