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Proceedings of the 21st Conference on Computer Science and Intelligence Systems (FedCSIS)

Annals of Computer Science and Information Systems, Volume 47

Mining Bureaucracy Debt: Process Intelligence for Evidence-Based Deregulation

DOI: http://dx.doi.org/10.15439/2026F8734

Citation: Wil M.P. van der Aalst (). Mining Bureaucracy Debt: Process Intelligence for Evidence-Based Deregulation. In M. Bolanowski, M. Ganzha, M. Grzegorowski, L. Maciaszek, M. Paprzycki, A. Paszkiewicz, D. Ślęzak (eds), Proceedings of the 21st Conference on Computer Science and Intelligence Systems (FedCSIS). ACSIS, Vol. 47, pages 1–11.

Full text

Abstract. Administrative processes accumulate complexity over time. New forms, controls, approvals, reporting duties, and verification steps are introduced in response to incidents, audits, legal changes, political demands, or digitalization projects. These additions are usually individually defensible, but they are rarely reassessed or removed. Surprisingly, digitalization often accelerates this accumulation. Instead of eliminating obsolete procedures, organizations frequently reproduce them in software, add new digital controls, and retain parallel manual practices. The result is bureaucracy debt: the growing burden created by administrative requirements whose original purpose may have weakened, disappeared, or become achievable in a simpler way. This paper introduces bureaucracy mining, the systematic use of process mining and process intelligence to identify, quantify, compare, and reduce bureaucracy debt. We distinguish four layers at which debt may arise (legal, organizational, digital, and operational) and show how each translation from rules to execution may introduce additional burden. We outline longitudinal and comparative forms of bureaucracy mining, emphasize the need for object-centric event data, and argue that process observability and event-data portability are prerequisites for meaningful analysis.

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