Trustworthy Artificial Intelligence and Process Mining: Challenges and Opportunities

AI compliance is fragmented across organizations: The paper identifies that trustworthy AI governance requires coordination between multiple departments, external partners, and different systems, creating gaps where compliance requirements can fall through the cracks and expose organizations to regulatory and reputational risks.

Process mining reveals hidden compliance problems: By applying process mining techniques to track how AI compliance actually happens in practice, organizations can gain "fact-based visibility" into where bottlenecks occur and identify compliance gaps that might otherwise remain hidden.

Multiple dimensions of trustworthy AI require oversight: Meeting responsible AI standards involves managing several complex requirements simultaneously—including data governance, testing, model quality assurance, transparency, accountability, and confidentiality—each involving multiple steps and human review points that are difficult to coordinate manually.

Automation can improve compliance monitoring: The paper demonstrates that process mining enables automated analysis and monitoring of AI compliance processes, potentially reducing manual oversight burden and enabling faster detection and remediation of compliance issues.

Visibility enables better risk management: By surfacing how compliance processes actually execute (rather than how they're supposed to execute), organizations can proactively address uncertainties and prevent compliance failures before they create regulatory or reputational damage.