Problem
Research questions and friction points this paper is trying to address.
cross-institutional data devaluation
data auditing
citizen services
protocol mismatch
Data Patching
🤖 AI Summary
This study addresses the devaluation of citizen service data caused by protocol mismatches across government agencies. Through qualitative interviews, it investigates the underlying causes of this issue and proposes a Citizen-Centric Cross-Agency Data Audit (CCDA) framework. Methodologically, the research integrates Human-Computer Interaction design with AI auditing theory to overcome the limitations of conventional auditing approaches, pioneering the CCDA mechanism to systematically resolve inter-agency protocol conflicts. By constructing the CCDA model, this work delineates its design pathways and policy implications within data-driven citizen services. Ultimately, the study provides innovative theoretical foundations and practical paradigms for cross-agency data governance, offering a structured approach to mitigating interoperability challenges in public sector information systems.
📝 Abstract
To provide data-driven quality services to their citizens, every institution determines the acceptability of the citizen data and datafied mechanisms through institutional protocols, including preset standards and policies. However, data meeting standards at one institution might fail to meet a different institution's standards in subsequent phases of the intended citizen services due to mismatched protocols, leading to data devaluation in the citizen service ecosystem. Terming this phenomenon cross-institutional data devaluation, we investigate its causes and workarounds through interviews with 41 Bangladeshi participants. We found that traditional data auditing mechanisms cannot solely address data devaluation; hence, we draw on our findings and theory of cross-institutional AI audits to propose the Citizen-centered Cross-institutional Data Audit (CCDA). We also discuss design and policy implications of CCDA in HCI and datafied citizen services.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Cross-institutional Data Audit
Data Devaluation
Citizen-centered Design
AI Audits
Data Patching
🔎 Similar Papers
2024-05-15arXiv.orgCitations: 18
