Introduction

With the Trump election in 2020 immigration has become a prominent political issue, both in the USA and in countries like Canada that are closely tied to it. In this panel we take a critical digital humanities or data feminism approach (D’Ignazio & Klein 2020) to issues related to immigration management, immigration discourse, and the types of tools whether Algorithmic Impact Assessments or Palantir. We are interested both in how digital humanities approaches can help study the discourse around events like the 2026 World Cup and how we can “read” advanced digital tools meant to manage immigrants and match them to jobs.

From Data to Deportation: The Political Consequences of Palantir’s ImmigrationOS in U.S. Governance (Eleyan Sawafta)

Since 2008, Palantir has secured nearly $3 billion in US federal funding (Dwoskin et al. 2025), including contracts in 2025 to develop “ImmigrationOS” (Bhuiyan 2025). ImmigrationOS is an operating system designed to serve U.S. Immigration and Customs Enforcement (ICE) by tracking migrants and accelerating deportations (Bhuiyan 2025).

Palantir’s activities have triggered widespread political scrutiny and public concern. Several individuals have been arrested for protesting Palantir’s involvement in immigration enforcement (Bhuiyan 2025). Critiques have emerged, such as those from Robert Reich (2025), who warns in The Guardian (2025) that Palantir poses a growing threat to democracy by enabling opaque surveillance infrastructures shielded from public accountability. Former Palantir employee Juan Sebastián Pinto has also spoken out, warning that the company’s tools endanger constitutional rights and disproportionately target Hispanic Americans (Dwoskin et al. 2025).

This paper asks: How do software platforms like Palantir embed paranoid epistemologies into systems of governance, and what are the ethical and political consequences for how states imagine and act upon migrant populations? Drawing on investigative journalism, surveillance and risk studies, Palantir’s public writings, and a critical reading of interface design, the paper adopts a case study methodology. Rather than supplying a technical audit, the analysis will involve a thematic examination of sources to uncover underlying epistemologies and narratives.

From Openness to Control: Canada’s Immigration Reforms and the Rebordering of North America (Huong Le)

Mark Carney’s Liberal government introduced two bills early in the 45th Canadian Parliament: Bill C-2 (the Strong Borders Act) and its streamlined counterpart, Bill C-12 (Strengthening Canada’s Immigration System and Borders Act). Together, we argue they signal the latest shift in a data-driven rebordering of North America aimed at managing US-Canada relations through “digital modernization”. Specifically, the latest approach combines themes of migration control, security, and trade evident from the post 9/11 response of the early 2000s in the NAFTA countries, to now include managing disruptions caused by U.S. President Donald Trump’s threatened tariffs on Canada and Mexico. This short paper examines these bills as components of the Canadian government’s plan to modernize border and migration management, presenting three key findings. First, they frame border management as an economic tool, connecting migration to domestic labour market stability and supply chain resilience. Second, parliamentary debates about the bills reveal tensions between administrative efficiency and procedural fairness, with limited mechanisms for humanitarian oversight. Third, language in the bills emphasizing “orderly and secure mobility” signals a normative shift towards utilitarian policymaking, balancing compassion with cost-effectiveness. By combining discourse and text analysis with policy review and examining legislative texts, parliamentary debates, and select print media coverage since the 45th Parliament began on May 26, 2025, this paper enhances understanding of the political economy of North America in an era of advanced digital technologies and American protectionism.

Borders, Surveillance, Human Rights, and the 2026 FIFA World Cup (Abdullah Al-Zubai)

The 2026 FIFA World Cup, co-hosted by Canada, the United States, and Mexico, will be the largest soccer tournament ever. Due to the tri-national setup of this World Cup, athletes, fans, and workers will be travelling between the three host countries. Thus, this sporting mega-event will put North America’s rapidly evolving migration systems to the test, especially considering changes since the inauguration of Donald Trump in 2025. This paper examines the perceptions and impact of Canadian, American, and Mexican immigration policies, enforcement practices, and surveillance technologies on who can participate in or attend the 2026 World Cup, and how these policies align with FIFA’s human rights commitments. Drawing on media reports, government documents, NGO correspondence, and FIFA reports from 2025, this study uses the North American World Cup as a case study to explore the politics of mobility and inclusion. Building on existing research on mega-events, this paper argues that biometrics and social media screening are reshaping understandings and practices related to immigration, visa, and border policies with human rights implications. This research adds to discussions on migration governance, surveillance, and human rights at sporting mega-events while offering recommendations for global sporting bodies, host governments, and civil society groups.

Algorithmic Impact Assessments (Augustine Farinola)

Canada's commitment to bilingualism in government services assumes semantic equivalence across English and French. This study tests that assumption by analyzing 47 Algorithmic Impact Assessments (AIAs) using computational text analysis and LLM-powered semantic matching. We discovered three forms of translation failure. First, discoverability gaps: while 38% of AI systems have bilingual documentation, inconsistent title translation makes French versions effectively invisible (initial analysis detected only 4%). Second, availability gaps: 23% of systems remain genuinely monolingual, violating Official Languages Act requirements. Third, semantic drift: when bilingual versions exist, governance concepts transform across languages.

Analyzing 18 bilingual pairs, we found systematic divergences in key governance terms. English versions emphasize "oversight" and "human judgment," while French versions use "surveillance" and emphasize "client explanation." The term "accountability" appears 8 times in English documents but 0 times in French, suggesting fundamental differences in how responsibility is framed. Data input descriptions in English provide legal justification ("lawfully collected"), while French versions offer only technical descriptions ("audio recordings"). These divergences reveal that translation is not neutral transmission but governance transformation.

These divergences reveal that translation is not neutral transmission but a transformation of governance. We conclude that Canada's AI governance suffers from an "untranslatability" problem requiring not just better translation, but conceptual alignment across languages.

Epistemic (In)justice, Algorithmic Bias, and Migration Management: A Case Study of GeoMatch in Canada (Ayushi Khemka)

In June 2025, Immigration, Refugees, and Citizenship Canada (IRCC) issued its 2025-2026 plan which included a new initiative to pilot a data-driven algorithmic tool called ‘GeoMatch.’ Developed by the Immigration and Policy Lab at Stanford University, GeoMatch operates by inputting the “historical data about past refugees and their integration outcomes” into the algorithm and developing predictive models for the settlement and success of those candidates. GeoMatch is to be piloted to manage the “skilled workers” entering through the Express Entry (EE) stream in Canada (Canada, 2025a). The algorithmic tool includes “an income prediction model, regional characteristics, users’ location and other preferences” to match candidates with a jurisdiction expected to yield the “strongest economic outcomes” in relation to their employment (Canada, 2025a).

This paper focuses on the case of GeoMatch in Canada, noting the proposed pilot speaks to a larger global digital turn towards AI in migration and integration management. While scholars have critiqued the sociopolitical and ethical implications of algorithm-based technologies in border management and migrant detention, few have examined how the technical configuration of these tools shapes immigrant integration policy. We address this gap by providing a critical reading of GeoMatch and ask: What risks and opportunities does GeoMatch pose for immigrant agency, AI ethics and biases? We conduct an algorithmic impact assessment of GeoMatch, analysing the epistemic structures underlying it. We find that while the software carries the potential for supporting employment and settlement outcomes, there is a risk that this model carries bias which raises ethical issues by amplifying the longstanding problem of newcomer mismatch in career counselling processes and resettlement. Given the lack of engagement with Express Entry candidates in GeoMatch’s design, its implementation limits migrants’ agency in decision-making, further pointing to an urgent need for practising an ethics of care in AI governance.

Case Management at Scale: Documentation in GCMS (Ralph Padilla)

The Global Case Management System (GCMS) structures how immigration applications in Canada are handled, reviewed, and recorded across multiple stages of administrative processing. While GCMS is commonly examined through released case notes, particularly in moments of refusal or appeal, this perspective treats the system primarily as an archive of outcomes. This paper instead examines GCMS as a procedural environment in which documentation operates prospectively, shaping how cases move, pause, and accumulate meaning over time.

Rather than approaching records as transparent representations of decision making, the paper conceptualizes GCMS documentation as an operational medium that coordinates administrative labor and institutional priorities (Albu & Flyverbom, 2019; Cornelius, 2019). Drawing on approaches in software and platform studies that privilege observability over access to internal code, the analysis focuses on how interfaces, fields, and routine documentation practices make certain forms of judgment actionable while rendering others peripheral (Rieder & Hofmann, 2020; Leerssen, 2024). Using close analysis of GCMS notes and procedural artifacts, the paper traces how workflow sequencing influences attention, verification practices, and the timing of discretionary review.