Summary of Guiding the Generation Of Counterfactual Explanations Through Temporal Background Knowledge For Predictive Process Monitoring, by Andrei Buliga et al.
Guiding the generation of counterfactual explanations through temporal background knowledge for Predictive Process Monitoring
by Andrei Buliga, Chiara Di Francescomarino, Chiara Ghidini, Ivan Donadello, Fabrizio Maria Maggi
First submitted to arxiv on: 18 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to generating counterfactual explanations for Predictive Process Monitoring is proposed, which considers temporal background knowledge and control flow relationships among events. By adapting state-of-the-art genetic algorithms-based techniques, the method ensures that generated counterfactuals satisfy temporal constraints while maintaining traditional quality metrics. This work fills a gap in the Explainability in Predictive Process Monitoring field by incorporating temporal knowledge into counterfactual generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI systems can provide explanations for their predictions, but when it comes to process monitoring, considering time and order matters. A new method generates “what if” scenarios that keep the timing correct, making sure events happen in the right order. This helps understand why a prediction was made or why something didn’t happen as expected. |
