mlsanityData to 14 Oct 2026

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

arXiv 2604.15514 ↗First posted 16 Apr 2026cs.AI cs.CY cs.HCcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 33 of its 132 references that are in the map. First posted on arXiv 16 Apr 2026.

Builds on

33 of 132 references.

References · 33

  1. 2018Datasheets for Datasetscited by 1,386
  2. 2018Model Cards for Model Reportingcited by 1,080
  3. 2020Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditingcited by 384
  4. 2018Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systemscited by 234
  5. 2019The Ethics of AI Ethics -- An Evaluation of Guidelinescited by 129
  6. 2024Black-Box Access is Insufficient for Rigorous AI Auditscited by 126
  7. 2021Understanding and Evaluating Racial Biases in Image Captioningcited by 108
  8. 2019Explainability Fact Sheets: A Framework for Systematic Assessment of Explainable Approachescited by 101
  9. 2018Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Makingcited by 67
  10. 2021Everyday algorithm auditing: Understanding the power of everyday users in surfacing harmful algorithmic behaviorscited by 50
  11. 2023Understanding accountability in algorithmic supply chainscited by 40
  12. 2022Understanding Practices, Challenges, and Opportunities for User-Engaged Algorithm Auditing in Industry Practicecited by 32
21 more
  1. 2022Worldwide AI Ethics: a review of 200 guidelines and recommendations for AI governancecited by 28
  2. 2020Biases in Generative Art -- A Causal Look from the Lens of Art Historycited by 26
  3. 2021Outlining Traceability: A Principle for Operationalizing Accountability in Computing Systemscited by 26
  4. 2022The Principles of Data-Centric AI (DCAI)cited by 26
  5. 2020A Human-Centered Review of the Algorithms used within the U.S. Child Welfare Systemcited by 23
  6. 2021A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child-Welfarecited by 23
  7. 2022Under the Morphosyntactic Lens: A Multifaceted Evaluation of Gender Bias in Speech Translationcited by 23
  8. 2024The "Colonial Impulse" of Natural Language Processing: An Audit of Bengali Sentiment Analysis Tools and Their Identity-based Biasescited by 16
  9. 2023Algorithmic Harms in Child Welfare: Uncertainties in Practice, Organization, and Street-level Decision-Makingcited by 12
  10. 2023Rethinking "Risk" in Algorithmic Systems Through A Computational Narrative Analysis of Casenotes in Child-Welfarecited by 10
  11. 2023The DSA Transparency Database: Auditing Self-reported Moderation Actions by Social Mediacited by 10
  12. 2024Lazy Data Practices Harm Fairness Researchcited by 10
  13. 2024Improving governance outcomes through AI documentation: Bridging theory and practicecited by 8
  14. 2022Generating Full Length Wikipedia Biographies: The Impact of Gender Bias on the Retrieval-Based Generation of Women Biographiescited by 7
  15. 2024Automated Transparency: A Legal and Empirical Analysis of the Digital Services Act Transparency Databasecited by 7
  16. 2024"This is not a data problem": Algorithms and Power in Public Higher Education in Canadacited by 6
  17. 2025The Datafication of Care in Public Homelessness Servicescited by 6
  18. 2025The "Who", "What", and "How" of Responsible AI Governance: A Systematic Review and Meta-Analysis of (Actor, Stage)-Specific Toolscited by 6
  19. 2023Content Moderation on Social Media in the EU: Insights From the DSA Transparency Databasecited by 5
  20. 2023The Ethics of AI Value Chainscited by 3
  21. 2023Assessing AI Impact Assessments: A Classroom Studycited by 2

Built on by

Not cited yet.