Explainable AI Enabled Inspection of Business Process Prediction Models

Explainability can reveal hidden problems in AI decision-making: The paper demonstrates that by examining explanations generated from "black-box" machine learning models used in business process prediction, organizations can detect potential issues with how these models make decisions—issues that wouldn't be visible from performance metrics alone.

Domain knowledge is essential for responsible AI inspection: The approach combines machine learning explanations with contextual knowledge from historical business process data, suggesting that effective AI oversight requires both technical transparency tools and deep understanding of the specific business context where AI is deployed.

Trust in automated business decisions requires transparency: The paper addresses a critical gap where modern machine learning models automate important business decisions without providing reasoning, and proposes that generating and inspecting explanations is necessary to build justified trust in these systems rather than blind reliance on their predictions.

New evaluation methods are needed beyond traditional accuracy metrics: The findings suggest that current ways of evaluating business prediction models are insufficient, and that organizations should develop "model reliability metrics" that incorporate explainability analysis to ensure AI systems are not just accurate, but also trustworthy and understandable to human decision-makers.