Counterfactual Explanations for Predictive Business Process Monitoring

Counterfactual explanations address a key gap in AI interpretability: Rather than just explaining why a prediction was made, the research shows that humans typically want to know why a prediction was made instead of another outcome—a distinction that existing explainable AI methods often miss in business process contexts.

Generic AI explanation techniques can produce unrealistic recommendations: The paper demonstrates that applying standard explainability methods directly to business process monitoring generates explanations that ignore real-world process constraints, potentially suggesting changes that are operationally impossible or impractical to implement.

LORELEY achieves high accuracy while ensuring feasibility: The proposed method achieves 97.69% average fidelity in approximating prediction models while incorporating control flow constraints, meaning the explanations it generates are both accurate and actually achievable within real business processes.

Process constraints are essential for trustworthy AI explanations: By embedding knowledge of how business processes actually work into the explanation generation process, the research highlights that responsible AI systems must account for domain-specific operational realities, not just statistical patterns.

Better explanations can increase adoption of AI in practice: The paper identifies lack of interpretability as a major barrier to deploying sophisticated prediction models in real business settings, suggesting that realistic, understandable explanations are critical for organizations to confidently use advanced AI systems.