automation

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Harvester: Why IRCC is Harvesting Your Submitted Application Documents With Their Latest Automation Tool

 

We have re-produced IRCC’s Harvester user guide from 2021 below (with additional redactions added to preserve passwords that were likely erroneously disclosed).

Harvester Program Guide_Redacted 2_Redacted FINAL

 

What is Harvester?

Per page 5 of the PDF, it is an automation tool that downloads eDOCs from GCMS and organizes (read: reorganizes) the file using clear detailed names. The use of Harvester has improved productivity in pre-assessment by over 25% with minimal training.

Like Chinook (and compatible with Chinook), it also uses an Excel interface and Microsoft Access. Documents are harvested in silos, allowing an Officer to secure, control, and monitor access to a file. Reading between the lines, the use of Microsoft Access also allows all documentation to be displayed on one horizontal screen (to be used , alongside GCMS, and Chinook in a streamlined way. 7-zip is used to encrypt the documentation and similar to Chinook there’s a deletion system after use. Importantly, there appears to be added security functions on who can access the documents and also a trail of records for auditing. I suspect that this could come in handy in future litigation with respect to whether documentation was considered or not. Some docs are excluded from Harvester – either purposely by an Officer where the visa officer does not need to review said doc OR if the harvest does not succeed. I was not able to gleam from my reading where harvests are unsuccessful but one must assume there would be some tech explanation.

Much like Chinook, it appears quite innocuous on the face. It speeds up assessment, heck even I could use a Harvester download and saving (automating) the organization of a file before I review – tasks we often leave to legal assistants and case managers.

However, there may me more than meets the eye. We’re getting a clearer picture of what the Officer actually sees in front of them when they render a decision. What the Chinook 3+ Platform looks like, the various tools and prompts that may or may not be providing information to guide a decision being rendered. Harvester is another one.

 

Takeaways

I would love feedback from our readers to see if they have any ideas but at this stage, I am looking at a couple major ones.

  1. Does the way we name and number our files mean anything any more? We often are creative with the way we try and flag specific names or combine documents, but how does Harvester extract or parse this apart? Is Harvester used (usable) on all apps or just select types that are already streamlined online?
  2. How meaningful is the ability to view the documents on Microsoft Access. From my understanding Harvester replaces the need to utilize other applications such as possibly PDF, Word, or an image reviewer. What does that mean for the way an Officer scrolls through various documents. What other tools does Microsoft Access provide in this regard (I’ve only watched a few online videos so maybe some of the tech-minded can advise);
  3. Why are there silos created for multiple applications? I am concerned again about this ability to string together various applications and harvest all at the same time. Is there a purpose to this? It would make very much sense within a family of applicants to be able to do so, but why would multiple applications un-related be harvested unless its simply to get the files ‘set up’ for review.

Would love for some of you to take a look at Harvester and let us know what you think!

 

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Coach Will: New Vocabulary Words Tomorrow’s Immigration Practitioners Will Need To Know

As a resource, and to buy time as I am writing more substantive blogs, I wanted to share a #CoachWill blog on new vocabulary, terminology that tomorrow’s immigration practitioners will need to know, learn, advise their clients on, and spend time with. I am still very much learning these terms and their impact, but it gives us a mutual starting point to grow our knowledge of how Canadian immigration law will be impacted moving forward:

 

Advanced Analytics: which is composed of both Predictive and Prescriptive components, consists of using computer technology to analyze past behaviours, with the goal of discovering patterns that enable predictions of future behaviours. With the aid of a team of computer science, data, IT, and program specialists, AA may result in the creation of a model that can perform risk triage and enable automated approvals on a portion of cases, thereby achieving significant productivity gains and reducing processing times. [As defined in IRCC’s China-Advanced Analytics TRV Privacy Impact Assessment]

Artificial Intelligence: Encompassing a broad range of technologies and approaches, Al is essentially the field of computer science dedicated to solving cognitive problems commonly associated with human intelligence, such as learning, problem solving, and pattern recognition. [As defined in IRCC’s Policy Playbook on Automation]

 

Automated decision support system: Includes any information technology designed to directly support a human decision-maker on an administrative decision (for example, by providing a recommendation), and/or designed to make an administrative decision in lieu of a human decision-maker. This includes systems like eTA or Visitor Record and Study Permit Extension automation in GCMS. [As defined in IRCC’s Policy Playbook on Automation]

 

Black Box: Opaque software tools working outside the scope of meaningful scrutiny and accountability. Usually deep learning systems. Their behaviour can be difficult to interpret and explain, raising concerns over explainability, transparency, and human control. [As defined in IRCC’s Policy Playbook on Automation]

 

Deep learning/neural network is a subset of machine learning, which is essentially a neural network with three or more layers. These neural networks attempt to simulate the behavior of the human brain—albeit far from matching its ability—allowing it to “learn” from large amounts of data. While a neural network with a single layer can still make approximate predictions, additional hidden layers can help to optimize and refine for accuracy. [As defined by IBM: https://www.ibm.com/cloud/learn/deep-learning#:~:text=Deep%20learning%20is%20a%20subset,from%20large%20amounts%20of%20data

 

Exploration zone: The exploration zone – also referred to as a “sandbox” – is the environment used for
research, experimentation and testing related to advanced analytics and Al. Data, codes and software
are isolated from those in production so that they can be tested securely.
“Fettering” of a decision-maker’s discretion: Fettering occurs when a decision-maker does not
genuinely exercise independent judgment in a matter. This can occur when a decision-maker binds
him/herself to a fixed rule of policy, another person’s opinion, or the outputs of a decision support
system. Although an administrative decision-maker may properly be influenced by policy considerations
and other factors, he or she must put his or her mind to the specific circumstances of the case and not
focus blindly on one input (e.g. a risk score provided by an algorithmic system) to the exclusion of other
relevant factors. [As defined in IRCC’s Policy Playbook on Automation]

 

“Fettering” of a decision-maker’s discretion: Fettering occurs when a decision-maker does not
genuinely exercise independent judgment in a matter. This can occur when a decision-maker binds
him/herself to a fixed rule of policy, another person’s opinion, or the outputs of a decision support
system. Although an administrative decision-maker may properly be influenced by policy considerations
and other factors, he or she must put his or her mind to the specific circumstances of the case and not
focus blindly on one input (e.g. a risk score provided by an algorithmic system) to the exclusion of other
relevant factors. [As defined in IRCC’s Policy Playbook on Automation]

 

Machine learning: A sub-category of artificial intelligence, machine learning refers to algorithms and statistical models that learn and improve from examples, data, and experience, rather than following pre-programmed rules. Machine learning systems effectively perform a specific task without using explicit instructions, relying on models and inference instead. [As defined in IRCC’s Policy Playbook on Automation]

 

A minimum viable product (MVP) is a development technique in which a new product or website is developed with sufficient features to satisfy early adopters. The final, complete set of features is only designed and developed after considering feedback from the product’s initial users. [As defined by Techopedia – https://www.techopedia.com/definition/27809/minimum-viable-product-mvp

 

Predictive Analytics: brings together advanced analytics capabilities spanning ad-hoc statistical analysis, predictive modeling, data mining, text analysis, optimization, real-time scoring and machine learning. These tools help organizations discover patterns in data and go beyond knowing what has happened to anticipating what is likely to happen next. [As defined in IRCC’s China-Advanced Analytics TRV Privacy Impact Assessment]

 

Prescriptive Analytics: Prescriptive Analytics is an advanced analytics technology that can provide recommendations to decision-makers and help them achieve business goals by solving complicated optimization problems. [As defined in IRCC’s China-Advanced Analytics TRV Privacy Impact Assessment]

 

Process automation: Also called “business automation” (and sometimes even “digital transformation”), process automation is the use of digital technology to perform routine business processes in a workflow. Process automation can streamline a business for simplicity and improve productivity by taking mundane repetitive tasks from humans and giving them to machines that can do them faster. A wide variety of activities can be automated, or more often, partially automated, with human intervention maintained at strategic points within workflows. In the domain of administrative decision-making at IRCC, “process automation” is used in contrast with “automated decision support,” the former referring to straightforward administrative tasks and the latter reserved for activities involving some degree of judgment. [As defined in IRCC’s Policy Playbook on Automation]

[Last Updated: 19 April 2022 – we will continue to update as new terms get updated]

 

 

 

 

 

 

 

 

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Will Tao is an Award-Winning Canadian Immigration and Refugee Lawyer, Writer, and Policy Advisor based in Vancouver. Vancouver Immigration Blog is a public legal resource and social commentary.

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