pith. machine review for the scientific record. sign in

arxiv: 2604.15514 · v1 · submitted 2026-04-16 · 💻 cs.AI · cs.CY· cs.HC

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

Dipto Das , Christelle Tessono , Syed Ishtiaque Ahmed , Shion Guha This is my paper

Pith reviewed 2026-05-10 10:22 UTC · model grok-4.3

classification 💻 cs.AI cs.CYcs.HC
keywords AI transparencyCanadian AI Registerontological designalgorithmic decision-makingpublic sector AIbureaucratic accountabilitysociotechnical context
0
0 comments X

The pith

Canada's Federal AI Register frames government AI as reliable internal tooling by omitting human discretion and uncertainty.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines Canada's first Federal AI Register, released in November 2025, which lists 409 systems. It uses the ADMAPS framework to combine quantitative mapping with qualitative coding and finds that 86 percent of entries describe internal efficiency tools. The Register privileges technical descriptions while systematically leaving out details on human oversight, training processes, and how uncertainty is handled in practice. This design choice creates an image of AI as dependable equipment rather than as systems open to public challenge or debate over their decisions. If accurate, the finding implies that transparency registers can produce visibility without enabling real accountability for how these tools affect people.

Core claim

The Register is not a neutral record of government activity but an active instrument of ontological design that sets the boundaries of accountability. Analysis of the full 409-system dataset reveals a sharp gap between official rhetoric about sovereign AI and actual bureaucratic practice: most systems operate internally for efficiency gains, yet the Register consistently obscures the human discretion, training, and uncertainty management required to run them. By favoring technical specifications over sociotechnical context, the artifact constructs AI as reliable tooling instead of contestable decision-making.

What carries the argument

The Canadian Federal AI Register itself, treated as an ontological design artifact and examined through the Algorithmic Decision-Making Adapted for the Public Sector (ADMAPS) framework via quantitative mapping plus deductive qualitative coding.

If this is right

  • Transparency registers that omit sociotechnical details produce visibility without contestability.
  • The gap between sovereign-AI rhetoric and internal bureaucratic use becomes harder to address when registers hide operational realities.
  • Without redesign, such artifacts risk turning accountability into a routine compliance task rather than a site for public scrutiny.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Registers in other countries that follow similar technical-only templates may generate parallel blind spots about human roles in AI systems.
  • Adding required fields for training data sources, override procedures, and uncertainty handling could shift the ontology toward contestability.
  • Public debate over government AI might intensify if registers included concrete examples of how discretion is exercised in practice.

Load-bearing premise

The observed omissions and technical framing result from deliberate choices in how the transparency tool was built rather than from practical limits on what information agencies could or would report.

What would settle it

Direct examination of the internal data-submission guidelines or interviews with the officials who compiled the Register entries would show whether fields on human discretion and uncertainty were left out by design or because the data simply did not exist.

Figures

Figures reproduced from arXiv: 2604.15514 by Christelle Tessono, Dipto Das, Shion Guha, Syed Ishtiaque Ahmed.

Figure 1
Figure 1. Figure 1: A word cloud of the technical capabilities of the AI [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: Proportion of developers by GC organizations. [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper analyzes Canada's Federal AI Register (409 systems) via quantitative mapping and deductive qualitative coding under the ADMAPS framework. It claims the Register is not a neutral transparency tool but an active ontological design instrument that privileges technical descriptions, systematically obscures sociotechnical elements (human discretion, training, uncertainty management), and thereby constructs AI as 'reliable tooling' rather than 'contestable decision-making,' risking performative compliance without genuine accountability.

Significance. If the interpretive claims hold, the work offers a useful case study of how public-sector transparency artifacts can shape the boundaries of AI accountability, with direct relevance to ongoing policy debates on algorithmic registers and governance. The use of a complete public dataset and an external framework (ADMAPS) provides a replicable starting point for comparative analyses of similar registers.

major comments (2)
  1. [Abstract and Findings] The central inference—from observed omissions in the 409 entries to the conclusion that the Register 'constructs an ontology' via deliberate design choices—requires evidence distinguishing design intent from practical constraints. The manuscript reports the quantitative mapping and coding results but contains no analysis of the Register's mandatory fields, agency submission guidelines, data availability limits, or creation process; without this, the move from 'content X is missing' to 'the artifact was designed to obscure contestability' remains unsecured (see Abstract and the findings on internal deployment and obscured elements).
  2. [Methods] Deductive qualitative coding is described as central to identifying 'obscured' sociotechnical context, yet the manuscript provides no codebook, inter-coder agreement metrics, or concrete examples of how specific entries were coded as obscuring human discretion or uncertainty. This directly affects the verifiability of the strongest claim about ontological design (see Methods and the qualitative results sections).
minor comments (2)
  1. [Quantitative mapping] Clarify the exact operationalization of 'internal deployment' and 'sociotechnical context' when reporting the 86% figure to avoid ambiguity in cross-register comparisons.
  2. [Conclusion] The conclusion's policy recommendation on shifting design would be strengthened by a brief discussion of feasible alternatives (e.g., required fields for uncertainty or discretion) grounded in the observed data.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for these constructive comments, which identify key opportunities to strengthen the manuscript's evidential basis and methodological transparency. We address each major point below and outline the revisions we will make.

read point-by-point responses

  1. Referee: [Abstract and Findings] The central inference—from observed omissions in the 409 entries to the conclusion that the Register 'constructs an ontology' via deliberate design choices—requires evidence distinguishing design intent from practical constraints. The manuscript reports the quantitative mapping and coding results but contains no analysis of the Register's mandatory fields, agency submission guidelines, data availability limits, or creation process; without this, the move from 'content X is missing' to 'the artifact was designed to obscure contestability' remains unsecured (see Abstract and the findings on internal deployment and obscured elements).

    Authors: We agree that the manuscript would benefit from explicit discussion of the Register's structural constraints and development context to better ground the ontological-design claim. Our core argument centers on the published artifact's systematic patterns (identified via ADMAPS) rather than on designers' subjective intent; the Register, as released, privileges certain descriptions and thereby configures accountability boundaries. That said, the current text does not analyze mandatory fields, submission guidelines, or creation-process documentation. In revision we will add a dedicated subsection (likely in Background or Methods) drawing on all publicly available Government of Canada materials regarding the Register's rollout, field requirements, and agency instructions. This will allow readers to assess whether observed omissions reflect design features, data limitations, or both, while preserving the finding that the resulting transparency instrument performs ontological work. revision: yes

  2. Referee: [Methods] Deductive qualitative coding is described as central to identifying 'obscured' sociotechnical context, yet the manuscript provides no codebook, inter-coder agreement metrics, or concrete examples of how specific entries were coded as obscuring human discretion or uncertainty. This directly affects the verifiability of the strongest claim about ontological design (see Methods and the qualitative results sections).

    Authors: We accept this critique. Although the coding followed the ADMAPS framework deductively, the manuscript omitted the operational details needed for full replicability. In the revised version we will (1) append the complete codebook, (2) report inter-coder reliability statistics (e.g., Cohen's kappa or percentage agreement), and (3) include at least two to three anonymized, concrete examples of entries coded as obscuring human discretion or uncertainty management, with the exact ADMAPS codes applied. These additions will directly support the verifiability of the qualitative results without lengthening the main text. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation rests on external dataset analysis

full rationale

The paper conducts quantitative mapping and deductive qualitative coding on the complete public dataset of 409 Register entries using the external ADMAPS framework. The central claim—that the Register constructs an ontology of AI as reliable tooling—follows directly from observed patterns such as the 86% internal deployment rate and systematic absences of sociotechnical fields. No step reduces by construction to a fitted parameter, self-definition, or load-bearing self-citation chain; the argument is self-contained as an empirical reading of external data against a stated independent framework.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that the ADMAPS framework validly captures accountability dimensions and that register content directly reflects bureaucratic practice without needing external validation of internal operations.

axioms (1)
  • domain assumption The ADMAPS framework is an appropriate and sufficient lens for assessing public sector AI accountability and transparency.
    Used as the basis for both quantitative mapping and qualitative coding of the entire register.

pith-pipeline@v0.9.0 · 5496 in / 1257 out tokens · 55769 ms · 2026-05-10T10:22:07.331859+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

143 extracted references · 12 canonical work pages

  1. [1]

    Saleema Amershi, Dan Weld, Mihaela Vorvoreanu, Adam Fourney, Besmira Nushi, Penny Collisson, Jina Suh, Shamsi Iqbal, Paul N Bennett, Kori Inkpen, et al. 2019. Guidelines for human-AI interaction. InProceedings of the 2019 chi conference on human factors in computing systems. 1–13

    2019

  2. [2]

    Mike Ananny and Kate Crawford. 2018. Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability.new media & society20, 3 (2018), 973–989

    2018

  3. [3]

    Cecilia Aragon, Shion Guha, Marina Kogan, Michael Muller, and Gina Neff. 2022. Human-centered data science: an introduction. MIT Press

    2022

  4. [4]

    Starling Centre at Western University. 1 Fe. Tracking Automated Government ‘TAG’ Register Canada. http://tagregistercanada.ca/

  5. [5]

    Blair Attard-Frost. 2022. Comments on the 3rd Review of the Directive on Automated Decision-Making.A vailable at SSRN 4238164(2022)

    2022

  6. [6]

    Blair Attard-Frost, Ana Brandusescu, and Kelly Lyons. 2024. The governance of artificial intelligence in Canada: Findings and opportunities from a review of 84 AI governance initiatives.Government Information Quarterly41, 2 (June 2024), 101929. doi:10.1016/j.giq.2024.101929

    work page doi:10.1016/j.giq.2024.101929 2024

  7. [7]

    Blair Attard-Frost, Ana Brandusescu, David Gray Widder, and Christelle Tessono

  8. [8]

    https://techpolicy.press/ai-countergovernance-lessons-learned- from-canada-and-paris

    AI Countergovernance: Lessons Learned from Canada and Paris | Tech- Policy.Press. https://techpolicy.press/ai-countergovernance-lessons-learned- from-canada-and-paris

  9. [9]

    Blair Attard-Frost and David Gray Widder. 2025. The ethics of AI value chains.Big Data & Society12, 2 (June 2025), 20539517251340603. doi:10.1177/ 20539517251340603

    2025

  10. [10]

    Stefan Baack. 2024. A critical analysis of the largest source for generative ai training data: Common crawl. InProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency. 2199–2208

    2024

  11. [11]

    Ricardo Baeza-Yates. 2020. Bias in search and recommender systems. InPro- ceedings of the 14th ACM Conference on Recommender Systems. 2–2

    2020

  12. [12]

    Nagadivya Balasubramaniam, Marjo Kauppinen, Antti Rannisto, Kari Hiekka- nen, and Sari Kujala. 2023. Transparency and explainability of AI systems: From ethical guidelines to requirements.Information and Software Technology159 (2023), 107197

    2023

  13. [13]

    Yoshua Bengio, Sören Mindermann, Daniel Privitera, Tamay Besiroglu, Rishi Bommasani, Stephen Casper, Yejin Choi, Philip Fox, Ben Garfinkel, Danielle Goldfarb, et al . 2025. International AI safety report.arXiv preprint arXiv:2501.17805(2025)

    work page arXiv 2025

  14. [14]

    Reuben Binns. 2018. Algorithmic accountability and public reason.Philosophy & technology31, 4 (2018), 543–556

    2018

  15. [15]

    William Boag, Harini Suresh, Bianca Lepe, and Catherine D’Ignazio. 2022. Tech worker organizing for power and accountability. InProceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency. 452–463

    2022

  16. [16]

    2021.Artificial Intelligence Policy and Funding in Canada: Pub- lic Investments, Private Interests

    Ana Brandusescu. 2021.Artificial Intelligence Policy and Funding in Canada: Pub- lic Investments, Private Interests. Technical Report. Centre for Interdisciplinary Research on Montreal, McGill University

    2021

  17. [17]

    Ana Brandusescu. 2025. Transjurisdictional AI Governance and the Myth of Sovereign AI. Invited presentation at the Taiwan–Canada Frontiers of Science Symposium. Co-hosted by CIFAR, DSET, and NSTC

    2025

  18. [18]

    Ana Brandusescu and Renee Sieber. 2022. Comments on the third review of Canada’s Directive on Automated Decision-Making.A vailable at SSRN 4271913 (2022)

    2022

  19. [19]

    Ana Brandusescu and Renée E. Sieber. 2025. Missed opportunities in AI regula- tion: lessons from Canada’s AI and data act.Data & Policy7 (Jan. 2025), e40. doi:10.1017/dap.2025.17

    work page doi:10.1017/dap.2025.17 2025

  20. [20]

    Dennis Broeders and Huub Dijstelbloem. 2015. The Datafication of mobility and migration management: The mediating state and its consequences. InDigitizing identities. Routledge, 242–260

    2015

  21. [21]

    Anna Brown, Alexandra Chouldechova, Emily Putnam-Hornstein, Andrew Tobin, and Rhema Vaithianathan. 2019. Toward algorithmic accountability in public services: A qualitative study of affected community perspectives on algorithmic decision-making in child welfare services. InProceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 1–12

    2019

  22. [22]

    Lisa Ruth Brunner and Wei William Tao. 2024. Artificial intelligence and automation in the migration governance of international students: An accidental ethnography.Journal of International Students14, 1 (2024), 269–288

    2024

  23. [23]

    Joy Buolamwini and Timnit Gebru. 2018. Gender shades: Intersectional accu- racy disparities in commercial gender classification. InConference on fairness, accountability and transparency. PMLR, 77–91

    2018

  24. [24]

    Stephen Casper, Carson Ezell, Charlotte Siegmann, Noam Kolt, Taylor Lynn Curtis, Benjamin Bucknall, Andreas Haupt, Kevin Wei, Jérémy Scheurer, Marius Hobbhahn, et al. 2024. Black-box access is insufficient for rigorous AI audits. InProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency. 2254–2272

    2024

  25. [25]

    Jennifer Cobbe, Michael Veale, and Jatinder Singh. 2023. Understanding account- ability in algorithmic supply chains. InProceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency. 1186–1197

    2023

  26. [26]

    Nicholas Kluge Corrêa, Camila Galvão, James William Santos, Carolina Del Pino, Edson Pontes Pinto, Camila Barbosa, Diogo Massmann, Rodrigo Mambrini, Luiza Galvão, Edmund Terem, et al. 2023. Worldwide AI ethics: A review of 200 guidelines and recommendations for AI governance.Patterns4, 10 (2023)

    2023

  27. [27]

    Colonial Impulse

    Dipto Das, Shion Guha, Jed R Brubaker, and Bryan Semaan. 2024. The“Colonial Impulse" of Natural Language Processing: An Audit of Bengali Sentiment Anal- ysis Tools and Their Identity-based Biases. InProceedings of the 2024 CHI Con- ference on Human Factors in Computing Systems. 1–18

    2024

  28. [28]

    Jol" or

    Dipto Das, Carsten Østerlund, and Bryan Semaan. 2021. " Jol" or" Pani"?: How Does Governance Shape a Platform’s Identity?Proceedings of the ACM on Human-Computer Interaction5, CSCW2 (2021), 1–25

    2021

  29. [29]

    Lina Dencik, Arne Hintz, Joanna Redden, and Harry Warne. 2018. Data scores as Governance: Investigating uses of citizen scoring in public services project report. (2018)

    2018

  30. [30]

    Wesley Hanwen Deng, Boyuan Guo, Alicia Devrio, Hong Shen, Motahhare Eslami, and Kenneth Holstein. 2023. Understanding practices, challenges, and opportunities for user-engaged algorithm auditing in industry practice. InPro- ceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 1–18

    2023

  31. [31]

    Emily Denton, Alex Hanna, Razvan Amironesei, Andrew Smart, and Hilary Nicole. 2021. On the genealogy of machine learning datasets: A critical history of ImageNet.Big Data & Society8, 2 (2021), 20539517211035955

    2021

  32. [32]

    Daria Dergacheva, Vasilisa Kuznetsova, Rebecca Scharlach, and Christian Katzenbach. 2023. One Day in Content Moderation: Analyzing 24 h of Social Media Platforms’ Content Decisions through the DSA Transparency Database. (2023)

    2023

  33. [33]

    Nicholas Diakopoulos. 2015. Algorithmic accountability: Journalistic investiga- tion of computational power structures.Digital journalism3, 3 (2015), 398–415

    2015

  34. [34]

    Nicholas Diakopoulos. 2016. Accountability in algorithmic decision making. Commun. ACM59, 2 (2016), 56–62

    2016

  35. [35]

    Nicholas Diakopoulos. 2025. Prospective Algorithmic Accountability and the Role of the News Media. InComputer Ethics Across Disciplines: Deborah G. Johnson and Algorithmic Accountability. Springer, 125–143

    2025

  36. [36]

    Chiara Patricia Drolsbach and Nicolas Pröllochs. 2024. Content moderation on social media in the EU: Insights from the DSA Transparency Database. In Companion Proceedings of the ACM Web Conference 2024. 939–942

    2024

  37. [37]

    2018.Automating inequality: How high-tech tools profile, police, and punish the poor

    Virginia Eubanks. 2018.Automating inequality: How high-tech tools profile, police, and punish the poor. Macmillan+ ORM

    2018

  38. [38]

    Florian Eyert and Paola Lopez. 2023. Rethinking transparency as a commu- nicative constellation. InProceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency. 444–454

    2023

  39. [39]

    Norman Fairclough. 2023. Critical discourse analysis. InThe Routledge handbook of discourse analysis. Routledge, 11–22

    2023

  40. [40]

    Angela Fan and Claire Gardent. 2022. Generating Biographies on Wikipedia: The Impact of Gender Bias on the Retrieval-Based Generation of Women Biographies. InProceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Dublin, Ireland, 8561–8576. doi:10.18653/v1...

    work page doi:10.18653/v1/2022.acl-long.586 2022

  41. [41]

    Heike Felzmann, Eduard Fosch Villaronga, Christoph Lutz, and Aurelia Tamò- Larrieux. 2019. Transparency you can trust: Transparency requirements for artificial intelligence between legal norms and contextual concerns.Big Data & Society6, 1 (2019), 2053951719860542

    2019

  42. [42]

    Ben Gansky and Sean McDonald. 2022. CounterFAccTual: How FAccT under- mines its organizing principles. InProceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency. 1982–1992

    2022

  43. [43]

    Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé Iii, and Kate Crawford. 2021. Datasheets for datasets.Commun. ACM64, 12 (2021), 86–92

    2021

  44. [44]

    Mélanie Gornet, Simon Delarue, Maria Boritchev, and Tiphaine Viard. 2024. Mapping AI ethics: a meso-scale analysis of its charters and manifestos. In Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Trans- parency. 127–140

    2024

  45. [45]

    Samuel Groesch, Alena Birrer, Natascha Just, and Florian Saurwein. 2025. Big data, small answers: How the DSA Transparency Database falls short of its regulatory objectives.Telecommunications Policy(2025), 103088

    2025

  46. [46]

    M Haataja, L van de Fliert, and P Rautio. 2020. Public AI Registers: Realising AI transparency and civic participation in government use of AI.Whitepaper Version1 (2020)

    2020

  47. [47]

    Thilo Hagendorff. 2020. The ethics of AI ethics: An evaluation of guidelines. Minds and machines30, 1 (2020), 99–120

    2020

  48. [48]

    Charlotte Högberg. 2024. Stabilizing translucencies: Governing AI transparency by standardization.Big Data & Society11, 1 (2024), 20539517241234298

    2024

  49. [49]

    Mohammad Hossein Jarrahi, Ali Memariani, and Shion Guha. 2023. The Principles of Data-Centric AI.Commun. ACM66, 8 (July 2023), 84–92. FAccT ’26, June 25–28, 2026, Montréal, Canada Dipto Das, Christelle Tessono, Syed Ishtiaque Ahmed, and Shion Guha doi:10.1145/3571724

    work page doi:10.1145/3571724 2023

  50. [50]

    Deborah G Johnson. 2021. Algorithmic accountability in the making.Social Philosophy and Policy38, 2 (2021), 111–127

    2021

  51. [51]

    Nari Johnson and Hoda Heidari. 2023. Assessing AI Impact Assessments: A Classroom Study.arXiv preprint arXiv:2311.11193(2023)

    work page arXiv 2023

  52. [52]

    Rishabh Kaushal, Jacob Van De Kerkhof, Catalina Goanta, Gerasimos Spanakis, and Adriana Iamnitchi. 2024. Automated transparency: A legal and empirical analysis of the digital services act transparency database. InProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency. 1121–1132

    2024

  53. [53]

    2022.Facial recognition technology and the growing power of artificial intelligence

    Pat Kelly. 2022.Facial recognition technology and the growing power of artificial intelligence. Parliament

    2022

  54. [54]

    Svetlana Kiritchenko and Saif Mohammad. 2018. Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems. InProceedings of the Seventh Joint Conference on Lexical and Computational Semantics. Association for Com- putational Linguistics, New Orleans, Louisiana, 43–53. doi:10.18653/v1/S18-2005

    work page doi:10.18653/v1/s18-2005 2018

  55. [55]

    Rob Kitchin and Gavin McArdle. 2016. What makes Big Data, Big Data? Explor- ing the ontological characteristics of 26 datasets.Big data & society3, 1 (2016), 2053951716631130

    2016

  56. [56]

    Katarzyhna Klafkowska-Waśniowska and Jakub Drąg. [n. d.]. In Search of Reason. Are Statements of Reason Under the Digital Services Act a Valuable Source of Information on Content Moderation Practices?A vailable at SSRN 5257655([n. d.])

  57. [57]

    Joshua A Kroll. 2021. Outlining traceability: A principle for operationalizing accountability in computing systems. InProceedings of the 2021 ACM Conference on fairness, accountability, and transparency. 758–771

    2021

  58. [58]

    Who", “What

    Blaine Kuehnert, Rachel Kim, Jodi Forlizzi, and Hoda Heidari. 2025. The“Who", “What", and“How" of Responsible AI Governance: A Systematic Re- view and Meta-Analysis of (Actor, Stage)-Specific Tools. InProceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency. 2991–3005

    2025

  59. [59]

    2025.Submission to the Consultation on Canada’s Renewed AI Strategy

    Mahtab Laghaei, Christelle Tessono, Viet Vu, and André Côté. 2025.Submission to the Consultation on Canada’s Renewed AI Strategy. Technical Report. The Dais, University of Toronto. https://dais.ca/reports/submission-to-the-consultation- on-canadas-renewed-ai-strategy/

    2025

  60. [60]

    Stefan Larsson and Fredrik Heintz. 2020. Transparency in artificial intelligence. Internet policy review9, 2 (2020), 1–16

    2020

  61. [61]

    Staffan I Lindberg. 2013. Mapping accountability: core concept and subtypes. International review of administrative sciences79, 2 (2013), 202–226

    2013

  62. [62]

    Tricia Lo. 2025. TELUS opens Canada’s first fully Sovereign AI Fac- tory. https://www.telus.com/en/about/news-and-events/media-releases/telus- opens-canadas-first-fully-sovereign-ai-factory

    2025

  63. [63]

    Shaina Luck. 2025. Immigration lawyers concerned IRCC’s use of processing technology leading to unfair visa refusals. https://www.cbc.ca/news/canada/ nova-scotia/immigration-canada-ircc-technology-1.7632130. [Accessed: Dec 28, 2025]

    2025

  64. [64]

    Kristian Lum and William Isaac. 2016. To predict and serve?Significance13, 5 (2016), 14–19

    2016

  65. [65]

    James G March. 1978. Bounded rationality, ambiguity, and the engineering of choice.The bell journal of economics(1978), 587–608

    1978

  66. [66]

    Tasmiah Tahsin Mayeesha, Farzana Islam, and Nova Ahmed. 2024. AI4Bangladesh: AI Ethics for Bangladesh-Challenges, Risks, Principles, and Suggestions. InProceedings of the 13th International Conference on Information & Communication Technologies and Development. 260–272

    2024

  67. [67]

    This is not a data problem

    Kelly McConvey and Shion Guha. 2024. " This is not a data problem": Algorithms and Power in Public Higher Education in Canada. InProceedings of the 2024 CHI Conference on Human Factors in Computing Systems. 1–14

    2024

  68. [68]

    Nora McDonald, Sarita Schoenebeck, and Andrea Forte. 2019. Reliability and inter-rater reliability in qualitative research: Norms and guidelines for CSCW and HCI practice.Proceedings of the ACM on human-computer interaction3, CSCW (2019), 1–23

    2019

  69. [69]

    Angelina McMillan-Major, Emily M Bender, and Batya Friedman. 2024. Data statements: From technical concept to community practice.ACM Journal on Responsible Computing1, 1 (2024), 1–17

    2024

  70. [70]

    Danaë Metaxa, Joon Sung Park, Ronald E Robertson, Karrie Karahalios, Christo Wilson, Jeff Hancock, Christian Sandvig, et al . 2021. Auditing algorithms: Understanding algorithmic systems from the outside in.Foundations and Trends® in Human–Computer Interaction14, 4 (2021), 272–344

    2021

  71. [71]

    2001.Methods of critical discourse analysis

    Michael Meyer and Ruth Wodak. 2001.Methods of critical discourse analysis. Sage London

    2001

  72. [72]

    Microsoft. n.d.. Canada Protected B. https://learn.microsoft.com/en-us/azure/ databricks/security/privacy/cccs-medium-protected-b. [Accessed: December 13, 2025]

    2025

  73. [73]

    Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasser- man, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru

  74. [74]

    InProceedings of the conference on fairness, accountability, and transparency

    Model cards for model reporting. InProceedings of the conference on fairness, accountability, and transparency. 220–229

  75. [75]

    2024.The Walls Have Eyes: Surviving Migration in the Age of Artificial Intelligence(first edition

    Petra Molnár. 2024.The Walls Have Eyes: Surviving Migration in the Age of Artificial Intelligence(first edition. ed.). New Press, The, New York

    2024

  76. [76]

    Erina Seh-Young Moon, Devansh Saxena, Dipto Das, and Shion Guha. 2025. The Datafication of Care in Public Homelessness Services. InProceedings of the 2025 CHI Conference on Human Factors in Computing Systems. 1–16

    2025

  77. [77]

    Black Box

    Maya Murad. 2021.Beyond the" Black Box": Enabling Meaningful Transparency of Algorithmic Decision-Making Systems through Public Registers. Ph. D. Disser- tation. Massachusetts Institute of Technology

    2021

  78. [78]

    Esther Nieuwenhuizen. 2024. Algorithm Registers: A Box-Ticking Exercise or Meaningful Tool for Transparency?Information Polity29, 4 (2024), 415–433

    2024

  79. [79]

    Chris Norval, Kristin Cornelius, Jennifer Cobbe, and Jatinder Singh. 2022. Dis- closure by Design: Designing information disclosures to support meaningful transparency and accountability. InProceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency. 679–690

    2022

  80. [80]

    Government of Bangladesh. 2024. National Artificial Intelligence Policy. https: //tinyurl.com/BDAIpolicy. Accessed: December 21, 2025

    2024

Showing first 80 references.