Bureaucratic Silences: What the Canadian AI Register Reveals, Omits, and Obscures

TL;DR

This paper critically examines Canada's AI Register, revealing how it constructs an ontology of AI that emphasizes technical reliability over sociotechnical context, thus affecting accountability and transparency.

Contribution

It introduces an analytical framework combining quantitative and qualitative methods to assess AI registers, highlighting their role in shaping perceptions of accountability and contestability.

Findings

86% of AI systems are used internally for efficiency.

The Register obscures human discretion and training involved in AI operation.

It constructs an ontology of AI as reliable tooling, not contestable decision-making.

Abstract

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.