发表机构
University of Illinois Urbana-Champaign; University of Toronto（伊利诺伊大学厄巴纳-香槟分校; 多伦多大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过访谈41名孟加拉国参与者，提出以公民为中心的跨机构数据审计（CCDA），以解决跨机构数据贬值问题。
AI 中文摘要
为了向公民提供数据驱动的优质服务，每个机构都通过机构协议（包括预设标准和政策）来确定公民数据和数据化机制的可接受性。然而，由于协议不匹配，在一个机构符合标准的数据可能在预期公民服务的后续阶段不符合另一机构的标准，导致公民服务生态系统中数据贬值。我们将这一现象称为跨机构数据贬值，并通过与41名孟加拉国参与者的访谈调查其原因和变通方法。我们发现传统的数据审计机制无法单独解决数据贬值问题；因此，我们基于研究结果和跨机构人工智能审计理论，提出了以公民为中心的跨机构数据审计（CCDA）。我们还讨论了CCDA在人机交互和数据化公民服务中的设计和政策影响。
英文摘要
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.
