Responsible artificial intelligence in international migration management: Legal and practical considerations
Bibliographic record
Abstract
Artificial intelligence (AI) technologies, including generative AI, have become increasingly prevalent in the daily lives of millions of individuals worldwide. Therefore, it is not surprising that governments use AI technologies, including generative AI, to streamline workloads and increase efficiency in migration processing. AI is understood here as “a machine‑based system that is designed to operate with varying levels of autonomy and that may exhibit adaptiveness after deployment, and that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments”. Generative AI is a subset of AI technologies which “create[s] new content … based on their training data and in response to prompts”. Generative AI enables the creation of various forms of content, including text, images, videos, music and software code. Some States have disclosed the use of AI, including generative AI, in international migration management. For example, Australia has acknowledged using AI to identify potential fraud in visa applications and support staff productivity and generative AI to synthesize and analyse large volumes of documentation. Canada has also been using AI to triage visa applications. Germany has utilized AI for identity management, including face, speech and dialect recognition; name transliteration (i.e. the conversion from one alphabet to another, such as from Arabic to Roman alphabet); and mobile phone data analysis. The European Union Pact on Migration and Asylum recognizes the use of facial recognition technologies in the context of the Eurodac regulation. However, not all States have publicly acknowledged whether and, if so, how they use AI in international migration management. Regarding the first point – whether States are using AI in this area – this paper argues that subjectivity. This may include considerations States should be more transparent, as this would help increase trust in their systems and processes and, ultimately, strengthen the rule of law. Regarding the second issue – how States use AI in this field – the paper reflects on the current advances in AI regulation worldwide and highlights the importance of adhering to international human rights law. Finally, it introduces a framework to support States with the responsible implementation of AI in international migration management.
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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 | 0.037 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.044 |
| Scholarly communication | 0.021 | 0.032 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.042 | 0.026 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".