Every year, Immigration, Refugees and Citizenship Canada (IRCC) uses automated tools to sort through hundreds of thousands of temporary resident visa applications — visitor visas, super visas, study permits — before a human officer ever opens the file.
The department says this speeds up routine cases and helps officers focus on complex ones. It also says the system never refuses an application on its own, and that officers remain in control of every final decision.
Applicants who are refused, delayed for months, or asked for additional documents say they often have no way of knowing whether any of that automated sorting happened to their case at all.
Researchers at the University of Toronto authored a study titled “Is This Not Enough?: Asymmetries in Institutional Accountability and Collective Sensemaking in the Case of Canada’s Algorithmic Visa Triage System” in which, compared IRCC’s own Algorithmic Impact Assessment for its temporary resident visa triage system against thousands of posts from Canadian immigration subreddits, where applicants trade advice and speculate about why their files were handled the way they were.
The paper’s authors found that while IRCC’s documentation emphasizes transparency, procedural safeguards and human oversight, applicants are left to find answers about what happened to them through guesswork and peer comparison — piecing together bank balance thresholds, property ownership requirements, and processing timelines from other people’s outcomes rather than their own.
The researchers describe this as an “epistemic asymmetry”: institutional interpretability, on paper, does not translate into anything applicants can actually use.
A second pattern, which they call jurisdictional asymmetry, tracks something applicants raise constantly online — whether nationality, the specific visa office or travel history quietly shapes how a file is treated, even though IRCC frames the system as uniform.
A third pattern captures how processing delays disrupt weddings, pregnancies, job offers and family reunification in ways the department’s own risk assessment rates as, at most, moderate and reversible.
IRCC said officers are trained to assess every application “fairly and consistently… regardless of country of origin,” and that its automated tools are “primarily rules-based”, operate under human oversight and do not make or alter decisions.
The department pointed to its Algorithmic Impact Assessments, AI Strategy and public AI Register as evidence of its transparency, and said refused applicants receive written reasons and can request reconsideration.
IRCC also said officers are not permitted to use generative AI tools when deciding an application — a claim the Toronto Star tested in March, reporting that a permanent residence applicant was refused after a generative-AI-assisted review fabricated details of her job duties.
Immigration lawyers who work with refused and delayed clients say that response, while accurate on its face, doesn’t answer the question their clients actually ask.
“There is no formal mechanism for an applicant to challenge an internal triage classification itself,” said Yameena Ansari, managing lawyer at Ansari Law. Applicants, she said, are rarely told how their file moved through the system, and there is no simple notation in the records they can access indicating whether automated triage played a role.
Zeynab Ziaie Moayyed, a certified specialist in citizenship and immigration law, put it more bluntly: in years of reviewing refusal files, she says she has seen risk-indicator language surface in officers’ notes only a handful of times among thousands of cases.
She points to internal IRCC figures suggesting officers spend under two minutes reviewing each temporary resident application, and argues that once a file is flagged as high-risk, officers become more likely to fall victim to automation bias and search for reasons to refuse it.
For most clients who are refused despite a complete application, judicial review in Federal Court is the only route left — a costly, adversarial process that only succeeds because, she says, “a human being has to look at the application and the refusal and be able to justify the refusal,” something she argues rarely happens meaningfully further upstream.
Cassandra Fultz of DF Immigration frames the problem as one the Supreme Court has already addressed, just not in this context. In Vavilov, the Court held that a reasonable administrative decision must be transparent, intelligible and responsive to the evidence — not “boilerplate.”
Fultz says she routinely sees refusals that state only that an applicant’s travel purpose “is not consistent with a temporary stay,” with no engagement with the evidence submitted. Whether that language originates with a human or is shaped by automation, she said, is almost beside the point if it fails the Vavilov standard either way.
This isn’t the first time Ottawa has faced this exact question. In 2022, a parliamentary committee investigated why study permit approval rates for French-speaking African applicants had fallen as low as 27 percent, concluding that racial bias was a factor. By 2023, IRCC’s own figures showed that rate had climbed to 34 percent — still trailing the 38 percent approval rate for English-speaking African applicants over the same stretch.
IRCC soon launched a new pilot in 2024 for “French-speaking international students from regions with lower study permit approval rates,” including Africa, by waiving the provincial attestation letter requirement and offering a permanent-residence pathway after graduation.
No comparable independent review has yet examined whether the newer AI triage system, specifically, produces similarly uneven outcomes.
“Regardless of how an application is triaged,” Fultz said, “every applicant is entitled to a decision that is transparent, reasonable, and capable of meaningful review.”
Shilpashree Jagannathan is a Hamilton-based freelance journalist and editor whose work has appeared in CBC News, New Canadian Media, The Margin, Business Insider, TRT World and Mint, and has been republished by The Walrus. She reports on immigration, labour, affordability, housing, elections, climate impacts and social justice, focusing on how policy and power shape the lives of households, workers, newcomers and local communities. Drawing on more than a decade in business journalism in India and a strong background in investigations, research and editorial work, she brings rigour and empathy to stories that trace the gap between policy and lived experience. She has also appeared on podcasts and radio programs to discuss her reporting and its broader public-policy implications.
