Data Migration in HCI: The Politics of Invisible Borders, Informal Networks, and Immigrants’ Data

Paper Title

Data Migration in HCI: The Politics of Invisible Borders, Informal Networks, and Immigrants’ Data

Publication Info

• Topic area: Sociotechnical processes of data migration and governance in Human-Computer Interaction (HCI).
• Keywords: Data migration, immigrants, HCI, epistemic injustice, informal networks, infrastructural violence, credential recognition, access theory, digital governance, migration systems.

Background and Problem

• Problem / challenge: Immigrants face systemic barriers in transferring and legitimizing personal and institutional data across borders, leading to exclusion from civic services and opportunities. Existing infrastructures often devalue or erase immigrants' histories and expertise.
• Significance: These barriers impact immigrants' access to housing, employment, healthcare, and social inclusion, perpetuating structural inequities and epistemic injustice.
• Motivation and related work: Prior HCI research has focused on usability and service design but has not adequately addressed data as a contested entity. Emerging critical scholarship highlights how data infrastructures encode power and reproduce inequalities, but gaps remain in understanding the sociotechnical processes of data migration and their implications for immigrant agency and inclusion.

Solution

• Proposed approach: The paper conceptualizes Data Migration as the active, political, and infrastructural process through which immigrants' data is transformed, reclassified, or dismissed across borders and institutions.
• Novelty:
  1. Empirical evidence of how data infrastructures act as invisible borders, reshaping immigrants' access to civic life.
  2. Extension of HCI scholarship by framing data migration as a process of epistemic injustice and infrastructural violence.
  3. Highlighting the role of informal networks in navigating systemic barriers and reclaiming data legitimacy.
  4. Proposing design principles for equitable data migration systems that legitimize diverse forms of evidence and integrate community practices.
• Procedure and key techniques:
  • Conducted semi-structured interviews with 32 immigrants in Canada.
  • Thematic analysis of participants' experiences with data-related barriers in financial, healthcare, educational, and bureaucratic systems.
  • Application of Ribot and Peluso’s Theory of Access to analyze mechanisms enabling or constraining access.

Results

• Concrete findings:
  • Immigrants encounter systemic barriers such as credit invisibility, credential devaluation, and truncated identities due to rigid data infrastructures.
  • Data gaps lead to cascading harms, including delays in employment, healthcare disruptions, and financial instability.
  • Informal networks (e.g., family, community groups) play a critical role in navigating bureaucratic opacity and infrastructural failures.
• Advantage over baselines:
  • Provides a longitudinal, cross-institutional perspective on data migration, highlighting how systemic barriers are reproduced across multiple regimes.
  • Extends prior HCI research by framing data migration as a sociotechnical phenomenon tied to power and epistemic injustice.
• Experiments / evaluation:
  • Interviews with immigrants from diverse backgrounds (Iran, Bangladesh, India, the Philippines, Ukraine) across four Canadian provinces.
  • Analysis of challenges in credential recognition, healthcare access, financial systems, and informal network reliance.
• Limitations and future work:
  • Focused on immigrant experiences in Canada; findings may not generalize to other contexts.
  • Qualitative methods limit statistical generalizability; future research could explore broader datasets and policy contexts.
  • Did not address voluntary invisibility, which warrants further investigation.

Summary

This paper introduces the concept of Data Migration to describe the sociotechnical and political processes through which immigrants' data is transformed and contested across borders. Through interviews with 32 immigrants in Canada, the study reveals systemic barriers in credential recognition, healthcare access, and financial systems, highlighting how data infrastructures act as invisible borders that perpetuate epistemic injustice. Informal networks emerge as critical infrastructures for navigating these barriers. The authors propose five design principles for equitable data migration systems, emphasizing the need to legitimize plural forms of evidence, integrate community practices, and ensure transparency and contestability. These findings contribute to HCI, migration studies, and critical data science by reframing data migration as a site of power and structural inequity.