While people register to Immigration, Refugees, and Citizenship Canada (IRCC), they generally have invested a considerable amount of time thoughtfully filling out paperwork and arranging for documents.
Immigrants anticipate that their requests will be processed by visa officers who will thoroughly evaluate the information presented to them. IRCC has not been forthcoming about how it uses technology to handle applications, moreover, the public is actually starting to get an understanding of metrics being executed through a sequence of ‘Access to Information Act’ requests as well as Federal Court of Canada litigation.
The IRCC effectively piloted automated processes based on predictive analytics that evaluated and automatically approved low-risk online temporary resident visa applications from China. The computer-controlled handling of certain types of applications is not a new concept.
As of 2015, most visa-exempt foreign nationals have had to apply for an Electronic Travel Authorization before flying to Canada. These applications, for the most part, were computer-controlled. Visa applications were divided into three tiers: low risk for auto-approval, medium risk for officer review, and high risk for officer review. This triage prototype was implemented for all applications from China in 2018, and it was also piloted in India that year.
The primary objective appears to be for machine learning to endorse minimal applications automatically, with officials only manually evaluating those designated as a medium to high risk. Furthermore, to automated triaging, IRCC has launched software that allows officials to process applications in bulk.
Chinook is the name of the software tool.
According to an affidavit filed in Federal Court by IRCC, Chinook is a stand-alone platform that greatly simplifies managerial stages. Data about applicants is derived from their applications and displayed in a spreadsheet. at the same time, various applications can be assessed on a single spreadsheet by visa officials. This enables them to evaluate the components of multiple applications on a single platform and complete administrative tasks via batch processes. It also permits visa officers to generate “risk indicators” and “local word flags” so that officers can identify potential applications of concern or priority in the processing queue.
According to the affidavit filed in Federal Court, when visa officers enter Chinook, a message appears that says, among other things, “The Chinook User Interface allows you to view multiple applications for review and initial assessment. It does not replace reviewing documents… and/or reviewing other information… The refusal notes generator is meant to assist with general bona fide refusals. If the notes do not reflect your refusal reasons, please write an individual note.”
Many people have expressed issues about the implementation of automated triaging and Chinook. These include the potential that it is what has resulted in increased rejection rates, that individual care is not being given to applications, that applications are not being carefully reviewed and instead are quickly bulk refused, that AI flagging a file as high-risk will lead to an officer simply affirming the AI’s finding, that refusal reasons are increasingly comprised of boilerplate frameworks, which is not helpful for applicants, and that it may propagate systemic problems.
Because IRCC has not been straightforward about the implementation of these systems and their outcomes, it is difficult to determine whether these concerns are valid.
Regardless, applicants must understand that Canada’s immigration system is no longer one in which human officers diligently transform individual applications in the order in which they are obtained. Because of IRCC’s use of artificial intelligence and bulk refusal generators, a review of the internal reasons or GCMS is often suggestive about whether such software was used, and whether a rejected candidate should file a reassessment proposal or attempt legal oversight to see if a human could make a different inference.
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