加拿大签证算法系统存在制度与民众感知的三重不对称，揭示算法治理的隐性不公。
"Is This Not Enough?": Asymmetries in Institutional Accountability and Collective Sensemaking in the Case of Canada's Algorithmic Visa Triage System
- 用ADMAPS框架分析制度透明度，结合Reddit讨论探究申请人集体解读机制。
- 发现决策逻辑不透明、地理位置决定曝光程度、等待时间体验差异三大不对称。
- 适合关注算法公平、跨境治理与公众感知的研究者和政策制定者阅读。
本文研究加拿大签证系统中算法问责的制度构建与申请人跨边界实际体验之间的差异。通过ADMAPS框架分析移民、难民与公民部（IRCC）针对临时居民签证（TRV）筛选系统的算法影响评估（AIA），并采用混合方法分析申请人社群在Reddit上的讨论。结果显示，尽管制度文件强调透明度、程序保障与影响范围可控，但申请人仍需依靠群体性意义建构来理解模糊决策，常依赖同伴经验应对不确定性。我们识别出三类不对称：认知层面的信息获取不对称、地缘政治地位导致的暴露程度不对称，以及等待与不确定感在时间与关系维度上的体验差异。研究强调应从制度设计转向关注公共部门算法治理中经验分布的不均衡性。这些发现表明，跨国移民背景下的算法治理会产生制度披露框架无法捕捉的结构性不对称，而扩展ADMAPS可更好反映问责机制的非对称转化。
原文摘要 · Abstract (English)
This paper examines how algorithmic accountability in Canada's visa system is articulated institutionally and experienced by applicants across borders. We analyzed Immigration, Refugees and Citizenship Canada (IRCC)'s Algorithmic Impact Assessment (AIA) for the temporary resident visa (TRV) triage system using the algorithmic decision-making adapted for the public sector (ADMAPS) framework and analyzed Reddit discussions among applicants using a mixed-methods approach. We show that while institutional artifacts emphasize transparency, procedural safeguards, and bounded impacts, applicants engage in collective sensemaking to interpret opaque decisions, often relying on peer knowledge amid uncertainty. We identify three asymmetries between how institutional accountability is structured and how people perceive the process: epistemic asymmetry in access to decision logic, jurisdictional asymmetry in exposure shaped by geopolitical positioning, and temporal--relational asymmetry in how waiting and uncertainty are experienced. We emphasize why it is important to shift attention from institutional design to the uneven distribution of experiences with public-sector algorithmic governance. Together, these contributions demonstrate how algorithmic governance systems in the context of transnational migration produce structured asymmetries not captured by institutional disclosure frameworks, and how extending ADMAPS can account for those uneven translations of accountability.
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