Generating Feasible and Plausible Counterfactual Explanations for Outcome Prediction of Business Processes
This work addresses the need for interpretable AI in business process analytics, though it is incremental as it builds on existing counterfactual methods by adapting them to sequential data.
The paper tackled the challenge of generating counterfactual explanations for opaque predictive models in business processes by introducing REVISEDplus, a data-driven approach that ensures counterfactuals are realistic and plausible, resulting in improved feasibility and plausibility as evaluated against defined validity properties.
In recent years, various machine and deep learning architectures have been successfully introduced to the field of predictive process analytics. Nevertheless, the inherent opacity of these algorithms poses a significant challenge for human decision-makers, hindering their ability to understand the reasoning behind the predictions. This growing concern has sparked the introduction of counterfactual explanations, designed as human-understandable what if scenarios, to provide clearer insights into the decision-making process behind undesirable predictions. The generation of counterfactual explanations, however, encounters specific challenges when dealing with the sequential nature of the (business) process cases typically used in predictive process analytics. Our paper tackles this challenge by introducing a data-driven approach, REVISEDplus, to generate more feasible and plausible counterfactual explanations. First, we restrict the counterfactual algorithm to generate counterfactuals that lie within a high-density region of the process data, ensuring that the proposed counterfactuals are realistic and feasible within the observed process data distribution. Additionally, we ensure plausibility by learning sequential patterns between the activities in the process cases, utilising Declare language templates. Finally, we evaluate the properties that define the validity of counterfactuals.