47 citations · 116 across the 12 of their papers we have counts for
13 papers
BPCE: A Prototype for Co-Evolution between Business Process Variants through Configurable Process Model
Linyue Liu, Xi Guo, Chun Ouyang +5
With the continuous development of business process management technology, the increasing business process models are usually owned by large enterprises. In large enterprises, diff…
Building Interpretable Models for Business Process Prediction using Shared and Specialised Attention Mechanisms
Bemali Wickramanayake, Zhipeng He, Chun Ouyang +3
In this paper, we address the "black-box" problem in predictive process analytics by building interpretable models that are capable to inform both what and why is a prediction. Pre…
Explainable AI Enabled Inspection of Business Process Prediction Models
Chun Ouyang, Renuka Sindhgatta, Catarina Moreira
Modern data analytics underpinned by machine learning techniques has become a key enabler to the automation of data-led decision making. As an important branch of state-of-the-art…
DiCE4EL: Interpreting Process Predictions using a Milestone-Aware Counterfactual Approach
Chihcheng Hsieh, Catarina Moreira, Chun Ouyang
Predictive process analytics often apply machine learning to predict the future states of a running business~process. However, the internal mechanisms of many existing predictive a…
Developing a Fidelity Evaluation Approach for Interpretable Machine Learning
Mythreyi Velmurugan, Chun Ouyang, Catarina Moreira +1
Although modern machine learning and deep learning methods allow for complex and in-depth data analytics, the predictive models generated by these methods are often highly complex,…
Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applications
Yu-Liang Chou, Catarina Moreira, Peter Bruza +2
There has been a growing interest in model-agnostic methods that can make deep learning models more transparent and explainable to a user. Some researchers recently argued that for…