"Is This Not Enough?": Asymmetries in Institutional Accountability and Collective Sensemaking in the Case of Canada's Algorithmic Visa Triage System

This paper reveals that Canada's algorithmic visa triage system creates significant asymmetries in accountability—specifically epistemic, jurisdictional, and temporal-relational gaps—between the institution's transparency-focused design and applicants' lived experiences of uncertainty, necessitating a shift in focus toward the uneven distribution of algorithmic governance impacts.

The Gist

Imagine you are trying to get a visa to visit Canada. You fill out a massive pile of paperwork, hoping to get a "yes." But instead of a human officer reading your story carefully, your application is first sorted by a digital robot. This robot decides how fast your file gets looked at and whether it gets a quick "yes" or needs more human attention.

This paper is about a big disconnect between how the government says this robot works and how the people actually feel when they use it.

Here is the story of that disconnect, broken down into simple parts.

1. The Two Different Worlds

The researchers looked at two very different sources of information to understand this system:

• The Government's "Rulebook" (Institutional Artifacts): They read the official documents, like the "Algorithmic Impact Assessment" (AIA). Think of this as the instruction manual written by the engineers and lawyers. It says, "We have safety checks! We have audits! The robot is transparent! We have human supervisors!" It's a very clean, organized, and reassuring document.
• The Applicants' "Water Cooler" (Reddit Discussions): They looked at thousands of posts on Reddit where visa applicants talk to each other. Think of this as a crowded waiting room where people are whispering, "Did you hear? If you have a US visa, you get faster service," or "I think you need exactly $1,500 in your bank account, not $1,400." This is messy, anxious, and full of guesses.

2. The Three Big Gaps (Asymmetries)

The paper found three major ways the "Rulebook" world and the "Waiting Room" world don't match up. The authors call these Asymmetries (unevenness).

A. The "Black Box" Guessing Game (Epistemic Asymmetry)

• The Government says: "Our system is transparent. We have clear rules and audit trails. You can see how it works."
• The Reality: The applicants can't actually see the rules. It's like a magic 8-ball. The government says the ball is clear, but to the applicant, it's just a black box.
• The Result: Because they can't see the rules, applicants have to play detective. They gather in online groups to build their own "folk theories." They guess, "Oh, if I own a house, I'll get approved," or "If I'm from Country X, I'll get rejected." They are trying to reverse-engineer a system they can't see, using only rumors and other people's stories. The government has the "truth," but the applicants only have "guesses."

B. The "Geography" Lottery (Jurisdictional Asymmetry)

• The Government says: "We treat everyone the same. The rules are uniform for everyone, no matter where you are."
• The Reality: The system treats people differently based on where they are standing.
• The Analogy: Imagine a security line at an airport. The sign says, "Everyone goes through the same scanner." But in reality, if you are standing in the VIP line, you get waved through. If you are standing in the "high-risk" line, you get patted down for an hour.
• The Result: Applicants from different countries (or with different passports) experience the system differently. Someone with a US passport might get a visa in a week, while someone from a different country waits months, even though the "rules" are supposedly the same. The government's documents don't really talk about how your location changes your luck.

C. The "Waiting Room" Torture (Temporal-Relational Asymmetry)

• The Government says: "This system is efficient. If there's a problem, it's a 'moderate' impact. It's just a delay, nothing serious."
• The Reality: Waiting isn't just "waiting." It's life on hold.
• The Analogy: Imagine you are waiting for a train. The train company says, "The delay is only 20 minutes." But for you, that 20 minutes means you miss your wedding, you can't start your new job, or your pregnant wife can't see her parents.
• The Result: The government counts "time" as a simple number on a spreadsheet. But for the applicant, time is tied to their family, their job, and their heart. A "moderate delay" in the government's eyes feels like a life-ruining crisis in the applicant's eyes. The government's report misses the emotional and relational weight of the wait.

3. The Big Lesson: We Need a New Map

The researchers argue that the current tools we use to check if these AI systems are fair (like the AIA) are like maps that only show the inside of the building. They show the hallways and the security cameras, but they don't show the people standing outside in the rain, wondering if they'll ever get in.

They propose a new framework called ADMATS (Algorithmic Decision-Making for Administrative Transnational Systems).

• The Old Map (ADMAPS): Looks at the bureaucracy, the humans, and the code.
• The New Map (ADMATS): Adds a fourth layer: The Transnational Asymmetry. It forces us to look at how the system feels to people on the other side of the border, acknowledging that "fairness" looks different depending on your passport, your bank account, and how long you've been waiting.

Summary

The paper asks: "Is this enough?"
The government says, "Yes, we have transparency and safety checks."
The applicants say, "No, we are still guessing, waiting, and feeling like the rules change depending on where we live."

The paper concludes that we can't just check the boxes on a government form to say a system is fair. We have to listen to the people living through the system, because their experience of "accountability" is very different from the government's definition of it.

Technical Summary

Technical Summary: "Is This Not Enough?": Asymmetries in Institutional Accountability and Collective Sensemaking in the Case of Canada's Algorithmic Visa Triage System

Problem Statement
Public-sector algorithmic governance, particularly in transnational domains like immigration, relies on institutional accountability frameworks (e.g., Algorithmic Impact Assessments or AIAs) to ensure transparency and procedural fairness. However, there is a critical gap in understanding how these institutional artifacts translate into the lived experiences of affected individuals. In the context of Canada's Immigration, Refugees and Citizenship Canada (IRCC), tools like the "Chinook" system and the "Advanced Analytics Triage of Overseas Temporary Resident Visa (TRV) Applications" are deployed to process visa applications. While IRCC and the National Research Council (NRC) characterize these systems as interpretable, procedurally safeguarded, and of "moderate" impact, applicants often face opaque decision-making processes. The core problem addressed is the disconnect between institutional claims of accountability (structured disclosure, technical interpretability) and the applicants' reality of uncertainty, leading to a reliance on collective sensemaking to navigate the system.

Methodology
The authors employ a comparative analytic design, treating two distinct data sources as sites where accountability is articulated and interpreted:

1. Institutional Artifacts: The IRCC's Algorithmic Impact Assessment (AIA) report for the TRV triage system and the NRC's peer review of the same system. These documents were analyzed using a deductive qualitative approach mapped to the ADMAPS (Algorithmic Decision-Making Adapted for the Public Sector) framework. ADMAPS conceptualizes governance across three dimensions: bureaucratic processes, human discretion, and algorithmic decision-making. The analysis focused on how risk, mitigation, and accountability are framed within these dimensions.
2. Applicant Discourse: A corpus of discussions from Canadian immigration-focused subreddits (e.g., r/immigrationCanada, r/CanadaVisa) collected via the Reddit API. This data was analyzed using a mixed-methods approach:
   • Computational Clustering: BERTopic was used to identify recurring themes, utilizing Qwen-3 for embeddings and HDBSCAN for clustering.
   • Qualitative Analysis: Manual review and grounded-theory coding were applied to clusters specifically related to TRV processes. This involved generating open codes (e.g., "guessing approval thresholds"), grouping them into axial codes (e.g., "inferring hidden decision criteria"), and synthesizing them into selective codes to identify structural patterns.

Key Results
The comparative analysis reveals systematic asymmetries between institutional design and applicant experience, categorized into three interrelated dimensions:

1. Epistemic Asymmetry: While institutional documents (AIA/NRC) emphasize technical interpretability (e.g., decision trees, audit trails, reproducibility), applicants lack access to the actual decision logic. Consequently, applicants engage in "collective sensemaking," reconstructing evaluative criteria through peer exchange, anecdotal comparison, and "folk theories." They attempt to infer hidden thresholds (e.g., required bank balances, property ownership) that are not explicitly disclosed, creating a parallel interpretive practice that compensates for the lack of actionable transparency.
2. Jurisdictional Asymmetry: Institutional documentation frames the triage system as uniformly applied based on complexity. However, applicants perceive their exposure to the system as heavily shaped by geopolitical positioning. Discussions reveal sensitivity to nationality, country of application, and prior visa history (e.g., holding a US visa), which applicants believe influences processing speed and outcomes. This suggests that identical formal rules produce uneven outcomes based on transnational positioning, a factor largely absent from institutional risk assessments.
3. Temporal–Relational Asymmetry: The AIA classifies the system's impact on rights and freedoms as "little to no impact" or "moderate," viewing delays as administrative inconveniences. In contrast, applicants experience waiting times as consequential, relational burdens that disrupt life trajectories (e.g., employment offers, family reunification, pregnancy care). The institutional assessment fails to account for time as a site of harm that is deeply entangled with social and familial dependencies.

Key Contributions

• Identification of Asymmetries: The paper empirically identifies three specific asymmetries (epistemic, jurisdictional, temporal-relational) that characterize the gap between public-sector algorithmic accountability frameworks and the transnational experience of migration.
• Critique of Current Frameworks: The study demonstrates that existing frameworks like ADMAPS, while effective for analyzing internal bureaucratic processes, are insufficient for capturing the uneven distribution of experiences in transnational contexts.
• Proposal of ADMATS: The authors propose an extension of the ADMAPS framework to ADMATS (Algorithmic Decision-Making for Administrative Transnational Systems). This new framework introduces a fourth dimension—Transnational Asymmetry—to explicitly account for how accountability is differentially produced and experienced across borders. ADMATS integrates the three identified asymmetries to analyze cross-border administrative algorithmic governance where institutional accountability and lived experience diverge.

Significance and Claims
The paper claims that existing governance instruments for public-sector AI, such as AIAs, fall short not due to their absence, but because accountability is unevenly interrogated and understood across bureaucratic and experiential contexts. The significance of this work lies in shifting the focus from institutional design and compliance to the "uneven distribution of experiences" with public-sector algorithmic governance.

The authors argue that for algorithmic governance to be truly accountable in transnational contexts, frameworks must move beyond technical interpretability and procedural safeguards to address how systems are experienced by populations situated outside the governing state. By introducing ADMATS, the paper provides a conceptual tool to analyze how structural conditions (geopolitics, time, knowledge access) shape the experience of algorithmic decision-making, highlighting that institutional claims of fairness and impact must be perceived as meaningful by the publics they govern to be effective. The work does not propose new technical implementations but rather a reframing of how accountability is conceptualized and evaluated in the context of transnational migration.