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

2026-04-16

AI Summary

This study examines how Canada’s federal AI Registry employs formalized transparency mechanisms to obscure accountability boundaries, particularly by neglecting the sociotechnical complexities inherent in human-AI collaborative decision-making. Drawing on an analysis of 409 AI systems—integrating quantitative mapping with deductive qualitative coding and applying the Algorithmic Decision-Making in the Public Sector (ADMAPS) framework—the research reveals that 86% of registered systems prioritize internal efficiency while largely omitting descriptions of human discretion and uncertainty management. The paper demonstrates for the first time that AI registries are not neutral record-keeping instruments but rather ontological design tools that actively shape accountability boundaries. Consequently, their transparency practices often amount to performative compliance, thereby diminishing opportunities for public contestation and oversight.

Technology Category

• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Humans and AI: AI for Accessibility

Application Category

• Responsible Web: Algorithmic accountability and transparency on the web
• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

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.

Problem

• AI transparency
• algorithmic accountability
• bureaucratic practice
• sociotechnical systems
• government AI register

Innovation

• ontological design
• algorithmic accountability
• sociotechnical systems
• AI governance
• transparency artifacts