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

2026-06-11

AI Summary

This study examines the structural asymmetry between institutional accountability mechanisms and applicants’ lived experiences within Canada’s algorithmic visa triage system. By extending the ADMAPS framework to incorporate cognitive, jurisdictional, and temporal-relational asymmetries, and drawing on mixed qualitative analysis of Reddit discourse, the research investigates how algorithmic governance produces unequal experiences in transnational migration contexts. Findings reveal a disconnect: while official narratives emphasize procedural transparency and institutional safeguards, applicants rely heavily on peer-generated knowledge to navigate opaque decision-making processes. This divergence underscores a systemic bias in existing accountability structures when applied across national boundaries, offering novel theoretical insights and empirical evidence on fairness in algorithmic governance.

Abstract

This paper examines how algorithmic accountability in Canada's visa system is articulated institutionally and experienced by applicants across borders. We analyzed Immigration, Refugees and Citizenship Canada (IRCC)'s Algorithmic Impact Assessment (AIA) for the temporary resident visa (TRV) triage system using the algorithmic decision-making adapted for the public sector (ADMAPS) framework and analyzed Reddit discussions among applicants using a mixed-methods approach. We show that while institutional artifacts emphasize transparency, procedural safeguards, and bounded impacts, applicants engage in collective sensemaking to interpret opaque decisions, often relying on peer knowledge amid uncertainty. We identify three asymmetries between how institutional accountability is structured and how people perceive the process: epistemic asymmetry in access to decision logic, jurisdictional asymmetry in exposure shaped by geopolitical positioning, and temporal--relational asymmetry in how waiting and uncertainty are experienced. We emphasize why it is important to shift attention from institutional design to the uneven distribution of experiences with public-sector algorithmic governance. Together, these contributions demonstrate how algorithmic governance systems in the context of transnational migration produce structured asymmetries not captured by institutional disclosure frameworks, and how extending ADMAPS can account for those uneven translations of accountability.

Problem

• algorithmic accountability
• institutional asymmetry
• collective sensemaking
• transnational migration
• algorithmic governance

Innovation

• algorithmic accountability
• asymmetry
• collective sensemaking
• ADMAPS
• transnational migration