IA dans la prise de décisions automatisée gouvernementale au Canada

Canadian federal and provincial government agencies are deploying AI and algorithmic tools in decisions about immigration, taxes, benefits, child welfare, and law enforcement — with inconsistent governance, limited transparency, and inadequate recourse for affected individuals.

The federal Directive on Automated Decision-Making (DADM), issued by the Treasury Board in 2019, provides a governance framework: it requires algorithmic impact assessments for automated decisions that affect the rights or interests of Canadians, establishes transparency requirements, and mandates human review mechanisms. However, compliance has been inconsistent. A 2022 Citizen Lab study documented gaps in DADM implementation across federal institutions — departments deploying AI tools without completing impact assessments, or categorizing systems in ways that minimized governance requirements.

Immigration, Refugees and Citizenship Canada (IRCC) has used data analytics to triage immigration applications since 2013, beginning with temporary resident visa backlogs, with machine learning-based triage formally deployed from 2017-2018. By 2024, IRCC's advanced analytics tools — including the "Chinook" case processing system and the "Automated Decision Assistant" — were processing millions of applications annually, sorting them into risk tiers that determine processing speed and scrutiny level. IRCC states that AI does not refuse applications, but the risk tiers materially shape outcomes: low-risk applications may receive streamlined processing (in some categories, positive decisions may be generated without officer review), while high-risk files receive additional scrutiny. Officers are not told how the tiering system works. Applicants have no way to know whether AI triage affected their case. The core concern is training data: if historical decisions contain patterns of refusal correlated with nationality, gender, age, or marital status, the AI reproduces those patterns at scale. Immigration lawyers have reported anecdotal patterns suggesting gender-based bias, including cases where single women were refused with reasons noting they were "young, single, and mobile."

Provincial and municipal government deployments have no equivalent framework. Quebec's Direction de la protection de la jeunesse used a risk assessment tool (SSP) that contributed to a child's death — a provincial deployment with no algorithmic impact assessment requirement. The CRA deployed an $18 million AI chatbot that the Auditor General found answered only 2 of 6 test questions correctly, while processing 18 million taxpayer queries.

Government AI deployment continues to outpace governance capacity. AI now shapes decisions about who gets a visa, who gets benefits, and which children are flagged as at risk — areas where transparency, assessment, and recourse are established governance expectations.