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

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

Source: https://arxiv.org/html/2604.15514

Dipto Das, Christelle Tessono
Faculty of Information
University of Toronto
Toronto, Ontario, Canada
[email protected]

Syed Ishtiaque Ahmed
Department of Computer Science
University of Toronto
Toronto, Ontario, Canada
[email protected]

Shion Guha
Faculty of Information
University of Toronto
Toronto, Ontario, Canada
[email protected]

(2026)

###### 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.

**Keywords:** AI Register, Government of Canada, Bureaucracy, Accountability, Transparency

## 1. Introduction

In November 2025, the Government of Canada (GC) publicly released its first government-wide artificial intelligence (AI) Register, disclosing over 400 AI systems used across more than 40 federal institutions. The GC highlighted making "public service more efficient" as the motivation for incorporating AI into federal government operations, with the register intended to reduce duplication and help departments identify opportunities to work more efficiently. Various geopolitical entities are building similar registers, such as the European Union (EU)'s Digital Services Act (DSA), positioning such disclosure infrastructures as a central mechanism for governing algorithmic systems used in the public sector.

From the algorithmic fairness, accountability, and transparency (FAccT) research perspective, we ask: *how meaningfully do AI registers enable accountability, or do they merely institutionalize its appearance?*

FAccT research has largely framed disclosure, through documentation standards, audits, and reporting requirements, as a necessary condition for oversight. While these approaches have advanced important norms (e.g., datasheets, model cards), critical scholarship has shown that institutional factors (e.g., incentives, structures) substantially shape disclosure efforts, such as AI registers. Moreover, emerging discourse on AI governance often treats AI systems as conceptually interchangeable, increasingly conflating generative and large language model (LLM)–based systems. This emphasis on highly visible and popular tools obscures the continued operation of long-standing predictive, scoring, and decision-support systems that continue to shape public-sector decision-making. By rendering these systems visible and classifying them in particular ways, AI registers shape how accountability is practiced, and more significantly, define what counts as AI in governance practice.

Rather than asking what information the Canadian AI Register discloses, we examine how it delineates responsibility, represents uncertainty, and locates discretion. To do so, we used the Algorithmic Decision-Making Adapted for the Public Sector (ADMAPS) framework, which foregrounds various types of interactions within public-sector governance.

Thus, this paper makes three contributions. First, we provide an empirical analysis of the complete Canadian federal Public AI Register to systematically examine 409 documented systems. Second, we introduce the concept of *bureaucratic silences* to describe how AI registers structure what is disclosed about public sector AI systems (e.g., tool- and developer-level disclosures, technical capabilities, and efficiency), and what remains illegible or off the record (e.g., human, bureaucratic, and contextual factors), particularly around discretion, infrastructure, and uncertainty. As similar registers emerge across Europe and beyond, the silences we identify and problematize should be examined as systemic properties of register-driven transparency regimes, rather than uniquely Canada-specific peculiarities—an insight that broadens the relevance of our analysis for global AI governance. Third, we argue that AI registers should be understood as instruments of *ontological design*, which shape how accountability is defined and enacted, with concrete implications for how future registers should be designed if they are to support meaningful democratic oversight and public trust in public-sector AI.

We situate our findings by discussing how the AI register contributes to and articulates Canada's preparedness for its national AI governance and sovereignty strategies.

## 2. Literature Review

Our literature review brings together three bodies of work that frame how algorithmic accountability is enacted in practice: sociotechnical perspectives on accountability and transparency, research on documentation and disclosure as governance infrastructures, and critical studies of public-sector algorithmic systems. We draw on these strands to motivate our choice of ADMAPS as the analytical lens for examining the Government of Canada AI Register.

### 2.1. Algorithmic Accountability and Transparency in Sociotechnical Systems

Accountability and transparency have emerged as central concepts in scholarship on the social impacts of algorithmic and AI systems. Whereas social sciences understand accountability as the everyday person's ability to hold an institution responsible, from a technical standpoint, accountability refers to the capacity of algorithmic systems to trace, justify, and assign responsibility for their decisions and outcomes, whereas transparency is commonly understood as the availability of information that enables explanation, documentation, or disclosure of system properties.

These concepts are often operationalized through technical and procedural interventions, such as explainable models, documentation frameworks, audit mechanisms, and reporting standards, which aim to render algorithmic systems more legible to developers, regulators, and affected users. Beyond such technical understandings of algorithmic accountability and transparency, scholars increasingly conceptualize them as shaped by sociocultural contexts, institutional logics, organizational structures, legal mandates, political priorities, and power asymmetries.

Within this line of critique, accountability is reframed as a question of governance: who is accountable to whom, through what mechanisms, and under what institutional conditions. For example, studies have shown that algorithmic supply chains fragment responsibility across vendors, developers, and deploying institutions, complicating traditional notions of oversight and liability. In such a scenario, where responsibility for algorithmic decisions is distributed across organizational roles, infrastructures, and decision points rather than residing solely in models or code, traceability emerges as a principle for operationalizing accountability.

In this governance-oriented view, transparency is not reducible to the disclosure of technical details but is better understood as a "communicative constellation" and a form of "disclosure by design," in which disclosures are situated, audience-specific, and shaped by institutional incentives, actively structuring how information is interpreted, which forms of accountability become possible, and who is empowered to act. Critical scholars have further highlighted the organizational stakes of accountability and transparency practices themselves, such as how disclosure datasets embody politics in defining what counts as accountable knowledge.

They showed how accountability is often pursued through collective action (e.g., advocacy) as a negotiation between efforts to strengthen accountability through disclosure and attempts to reduce regulatory burden. Algorithmic accountability depends on alignment among system design, professional judgment, and organizational context, and is an ongoing practice rather than a one-time technical intervention. Redden and colleagues have examined its extent in the public sector through case studies in fraud detection, child welfare, social services, and policing across Europe, North America, and Australia. In the Canadian context, Redden similarly investigated government discourses and practices surrounding big data adoption in the public sector using counter-mapping methods and freedom-of-information requests. These studies demonstrated significant gaps in existing transparency regimes since algorithmic governance is often difficult to trace due to fragmented documentation practices, institutional opacity, and limited proactive disclosure.

Overall, accountability is a relational and institutional practice that requires examining the documentation norms, disclosure regimes, and governance artifacts. Hence, researchers have called for systematic documentation and public disclosure mechanisms that enable oversight of algorithmic systems in governance.

### 2.2. Accountability in Governance through Documentation and Disclosure

Sociotechnically grounded AI governance research highlights how documentation and disclosure, operating at the intersection of technical practice and organizational governance, translate abstract accountability commitments into standardized, inspectable artifacts that circulate across organizations, regulators, and publics. Examples of these artifacts are documentation frameworks such as datasheets and model cards, which promote responsible AI development by standardizing disclosures about provenance, intended use, limitations, and risks. These frameworks also function as community practices, embedding disciplinary norms, institutional incentives, and implicit assumptions about responsibility and audience into their design and use.

However, documentation does not simply describe underlying systems—instead, it actively shapes which forms of information are legible and what kinds of accountability claims become possible. Therefore, data systems' accompanying documentation must be "read" critically: what decisions they encode about classification and how those would shape downstream interpretation and governance. Documentation practices not only support transparency and accountability but also delimit their scope by foregrounding certain risks, actors, and values while understating others.

Claims about scale, representativeness, or neutrality often become central to these documentations and obscure the contingent social and organizational conditions under which data are produced and maintained. For instance, scholars have traced how the discourse around data systems stabilizes narratives by shaping how influential benchmarks are conceptualized, reducing concerns about fairness and accountability to