ARTIFICIAL INTELLIGENCE IN MIGRATION POLICIES: RISKS AND OPPORTUNITIES FROM AN INTERNATIONAL LAW AND HUMAN RIGHTS PERSPECTIVE
Bibliographic record
Abstract
This article critically examines the integration of artificial intelligence (AI) into migration governance, focusing on the dual dimensions of efficiency gains and human rights challenges. The global rise in migration flows has prompted states to adopt advanced AI tools for border security, asylum adjudication, risk assessment, and migrant tracking. Case studies—including the EU’s Eurodac and ETIAS biometric systems, Australia’s “Seek” social media analysis project, and the U.S. CBP One facial recognition application—illustrate how AI enhances operational efficiency while raising significant ethical and legal questions. The study identifies three primary areas of concern under international law. First, algorithmic bias in migrant profiling and refugee status determination may violate the non-discrimination principle under Article 14 of the European Convention on Human Rights (ECHR) and the individual assessment requirement of the 1951 Refugee Convention. Second, the opacity of “black box” algorithms undermines transparency and accountability, restricting access to effective appeal mechanisms. Third, mass biometric surveillance, including iris scans at border crossings, presents acute data protection challenges, often conflicting with the EU’s General Data Protection Regulation (GDPR) and exposing migrants to cybersecurity breaches (Nuredin & Inan2024b). Despite these risks, AI offers notable humanitarian benefits, such as improving access to legal aid through AI-powered translation tools, enabling disaster-related evacuation planning, and fostering transparency via algorithmic impact assessments. The article highlights best practices from Canada’s mandatory ethical audits and Sweden’s explainable AI policies in migration decisionmaking. To reconcile innovation with human rights obligations, the article proposes a multilayered governance model: strengthening global standards such as the UNHCR AI Ethics Guidance, mandating human oversight in at least 30% of AI migration decisions, and adopting open-source, auditable algorithms. The overarching conclusion is that AI in migration management must remain human-centric, ensuring that technological advances serve as instruments of inclusion rather than exclusion, thereby safeguarding dignity, fairness, and the rule of law in the digital era.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.045 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.020 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".