Partisan Perspectives on AI and Immigration: An Analysis of Canadian Parliamentary Discourse (2014–2024)
Bibliographic record
Abstract
Abstract: In 2017, Canada became the first country to advance a national artificial intelligence (AI) strategy. In a now global race to adopt this technology, various stakeholders, including researchers and civil society organizations, have questioned its fairness in immigration, highlighting colonial legacies disguised as efficiency. These debates are particularly intense in the context of immigration decision-making due to the ethical and human rights implications, such as life-altering consequences for migrants and refugees. This article critically examines partisan discourse on AI and the immigration decisions on visa applications in Canada by analyzing parliamentary discussions. Specifically, extracting House of Commons Debates (Hansard) between 2014 and 2024, we analyze this data using Critical Discourse Analysis with the help of computational text analysis tools, including Voyant. We argue that while partisan differences exist in how AI is framed in Canada’s legislative debates on immigration, these debates overall fail to systematically engage with the full range of ethical and human rights implications, and continuing colonial legacies, embedded in the concerns raised by many researchers and stakeholders about immigration and artificial intelligence. Beyond the main argument, we observe that although some parties, like the Conservative Party, engaged in criticism, their interventions resembled protest more than a policy response of a “government-in-waiting” prepared to govern. Résumé: En 2017, le Canada est devenu le premier pays à avoir élaborer une stratégie nationale en matière d’intelligence artificielle (IA). Dans une course désormais mondiale pour adopter cette technologie, divers acteurs, notamment des chercheurs et des organisations de la société civile, ont remis en question son équité dans le domaine de l’immigration, soulignant des héritages coloniaux dissimulés sous le couvert de l’efficacité. Ces débats sont particulièrement vifs dans le contexte de la prise de décision en matière d’immigration, en raison des implications éthiques et des droits humains, telles que les conséquences déterminantes pour la vie des migrants et des réfugiés. Cet article examine de manière critique le discours partisan sur l’IA et les décisions d’immigration concernant les demandes de visa au Canada, en analysant les discussions parlementaires. Plus précisément, en extrayant les Débats de la Chambre des communes (Hansard) entre 2014 et 2024, nous analysons ces données à l’aide de l’Analyse critique du discours, soutenue par des outils d’analyse textuelle computationnelle, y compris Voyant. Nous soutenons que, bien que des différences partisanes existent dans la manière dont l’IA est présentée dans les débats législatifs canadiens sur l’immigration, ces débats ne parviennent pas, dans l’ensemble, à aborder de manière systématique l’ensemble des implications éthiques et des droits humains, ainsi que les héritages coloniaux persistants mis en lumière par de nombreux chercheurs et intervenants au sujet de l’immigration et de l’intelligence artificielle. Au-delà de l’argument principal, nous observons que, bien que certains partis, comme le Parti conservateur, aient formulé des critiques, leurs interventions s’apparentaient davantage à une forme de protestation qu’à une réponse politique d’un « gouvernement en attente » prêt à gouverner.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".