Explainable Predictive Process Monitoring

TL;DR

This paper introduces a method to provide explanations for predictive business process monitoring using Shapley Values, enhancing transparency in KPI predictions like remaining time and activity execution.

Contribution

It presents the first application of game theory-based explanations in predictive process monitoring, improving interpretability of predictions.

Findings

Effective explanation of predictions demonstrated on real-life benchmarks

Shapley Values provide robust and insightful explanations

Enhances trust and understanding of predictive models in business processes

Abstract

Predictive Business Process Monitoring is becoming an essential aid for organizations, providing online operational support of their processes. This paper tackles the fundamental problem of equipping predictive business process monitoring with explanation capabilities, so that not only the what but also the why is reported when predicting generic KPIs like remaining time, or activity execution. We use the game theory of Shapley Values to obtain robust explanations of the predictions. The approach has been implemented and tested on real-life benchmarks, showing for the first time how explanations can be given in the field of predictive business process monitoring.