TAPAS: A Pattern-Based Approach to Assessing Government Transparency

2025-05-22

AI Summary

Existing government transparency assessment methods suffer from two key limitations: they overlook actual civil servant behavioral patterns and rely on costly, non-scalable manual audits. To address these issues, this paper proposes TAPAS—a lightweight, automated, behavior-aware assessment framework grounded in behavioral anti-patterns. Leveraging two decades of real-world log data from an electronic document management system, TAPAS integrates design science methodology with behavioral pattern mining to systematically identify eight empirically grounded anti-patterns—grouped into four overarching categories—that undermine transparency. Unlike conventional approaches, TAPAS operates fully automatically, enabling continuous, low-overhead monitoring without human intervention. Empirical validation demonstrates its effectiveness, scalability, and minimal resource requirements. By shifting the focus from static policy compliance to dynamic behavioral analysis, TAPAS establishes a reusable, behavior-informed paradigm for governmental transparency evaluation.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Security and Privacy: Data transparency and provenance
• Responsible Web: Algorithmic accountability and transparency on the web
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Government transparency, widely recognized as a cornerstone of open government, depends on robust information management practices. Yet effective assessment of information management remains challenging, as existing methods fail to consider the actual working behavior of civil servants and are resource-intensive. Using a design science research approach, we present the Transparency Anti-Pattern Assessment System (TAPAS) -- a novel, data-driven methodology designed to evaluate government transparency through the identification of behavioral patterns that impede transparency. We demonstrate TAPAS's real-world applicability at a Dutch ministry, analyzing their electronic document management system data from the past two decades. We identify eight transparency anti-patterns grouped into four categories: Incomplete Documentation, Limited Accessibility, Unclear Information, and Delayed Documentation. We show that TAPAS enables continuous monitoring and provides actionable insights without requiring significant resource investments.

Problem

• Assessing government transparency via behavioral pattern identification
• Evaluating transparency using data-driven methods in public sector
• Identifying anti-patterns hindering effective information management practices

Innovation

• Data-driven methodology for transparency assessment
• Identifies behavioral patterns impeding transparency
• Enables continuous monitoring with low resources