加拿大首个AI注册表暴露了官僚系统对责任边界的刻意构建。
Bureaucratic Silences: What the Canadian AI Register Reveals, Omits, and Obscures
- 用定量与定性结合的方法分析409个系统，揭示透明度背后的权力设计。
- 86%的系统用于内部效率，但隐瞒了操作中的人为判断与不确定性管理。
- 适合关注数字治理、算法问责与政府透明度的读者。
2025年11月，加拿大政府发布首个联邦人工智能注册表，承诺透明化。本文指出，此类注册表并非中立的记录工具，而是主动建构责任边界的技术装置。我们采用ADMAPS框架，对注册表中409个系统的完整数据进行量化映射与演绎式定性编码。研究发现，'主权AI'的宣传话语与实际官僚实践存在显著背离：尽管86%的系统用于内部效率提升，但注册表系统性遮蔽了其运行所需的人为干预、训练过程及不确定性管理。通过优先呈现技术描述而忽略社会技术背景，注册表将AI塑造为‘可靠工具’而非‘可争议的决策机制’。若不改变设计逻辑，这类透明化举措恐将问责变成形式合规，带来可见却不可质疑的治理幻象。
原文摘要 · Abstract (English)
In November 2025, the Government of Canada operationalized its commitment to transparency by releasing its first Federal AI Register. In this paper, we argue that such registers are not neutral mirrors of government activity, but active instruments of ontological design that configure the boundaries of accountability. We analyzed the Register's complete dataset of 409 systems using the Algorithmic Decision-Making Adapted for the Public Sector (ADMAPS) framework, combining quantitative mapping with deductive qualitative coding. Our findings reveal a sharp divergence between the rhetoric of "sovereign AI" and the reality of bureaucratic practice: while 86\% of systems are deployed internally for efficiency, the Register systematically obscures the human discretion, training, and uncertainty management required to operate them. By privileging technical descriptions over sociotechnical context, the Register constructs an ontology of AI as "reliable tooling" rather than "contestable decision-making." We conclude that without a shift in design, such transparency artifacts risk automating accountability into a performative compliance exercise, offering visibility without contestability.
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