Guiding the generation of counterfactual explanations through temporal background knowledge for Predictive Process Monitoring

Counterfactual explanations need real-world constraints: The paper demonstrates that when AI systems explain their decisions by suggesting "what if" scenarios, these suggestions must respect the actual rules and sequences of how processes work. For example, in business workflows, you can't suggest changing the order of events in ways that violate how those processes actually operate.

Temporal relationships matter for trustworthy explanations: The research shows that previous methods for generating counterfactual explanations ignored the timing and sequencing rules that govern real processes. By incorporating these "temporal background knowledge" constraints, the explanations become more realistic and actionable for users trying to understand AI decisions.

Technical improvements maintain explanation quality: The authors adapted genetic algorithm techniques to respect temporal constraints while generating counterfactuals, and found that adding these real-world rules did not compromise the quality of explanations—they became more practical without sacrificing traditional performance metrics.

Explainability requires domain expertise: The work highlights that responsible AI explanations in process monitoring cannot be generated in a vacuum; they require understanding the actual business rules and constraints that govern how processes unfold, suggesting that AI systems need to be informed by domain knowledge to provide trustworthy explanations.