DIGITALIZATION AND GLOBAL MIGRATION: DATA SECURITY, ETHICS, AND INTERNATIONAL LAW
Bibliographic record
Abstract
The rapid digitalisation of migration governance has transformed how states manage mobility, security, and border control. From biometric databases and AI-driven decision-making systems to blockchain-based digital identities, these innovations promise efficiency, cost reduction, and enhanced risk assessment. However, they also introduce significant human rights challenges, particularly regarding privacy, due process, and equality before the law. This article examines the evolving interplay between emerging migration technologies and international legal standards, with a focus on the balance between security imperatives and the protection of migrant rights. Drawing on case studies such as the EU’s Eurodac system, Canada’s algorithmic visa processing, and UNHCR’s digital identity projects, the analysis identifies risks of algorithmic bias, discriminatory profiling, and mass surveillance. Legal frameworks, including the 1951 Refugee Convention, the European Convention on Human Rights, and the Council of Europe’s Convention 108, are assessed for their adaptability to the digital era. The study also explores positive applications, such as AI-assisted legal aid and humanitarian logistics planning, which highlight the potential for ethical innovation. Policy recommendations emphasise the need for a Digital Migrant Rights Charter, mandatory human oversight in algorithmic decision-making, and the adoption of transparent, auditable AI systems. Technological safeguards, including homomorphic encryption and open-source development, are proposed to protect sensitive migrant data while ensuring accountability. A multi-stakeholder governance approach—integrating governments, technology providers, civil society, and international organisations—is advocated as essential for aligning technological progress with international human rights obligations. By situating the debate within both ethical and legal contexts, the article contributes to the emerging discourse on responsible digital governance in migration, underscoring that technological innovation must be guided by principles that preserve human dignity in an increasingly data-driven world.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".