Technology on the margins: AI and global migration management from a human rights perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Experiments with new technologies in migration management are increasing. From Big Data predictions about population movements in the Mediterranean, to Canada's use of automated decision-making in immigration and refugee applications, to artificial-intelligence lie detectors deployed at European borders, States are keen to explore the use of new technologies, yet often fail to take into account profound human rights ramifications and real impacts on human lives. These technologies are largely unregulated, developed and deployed in opaque spaces with little oversight and accountability. This paper examines how technologies used in the management of migration impinge on human rights with little international regulation, arguing that this lack of regulation is deliberate, as States single out the migrant population as a viable testing ground for new technologies. Making migrants more trackable and intelligible justifies the use of more technology and data collection under the guise of national security, or even under tropes of humanitarianism and development. The way that technology operates is a useful lens that highlights State practices, democracy, notions of power, and accountability. Technology is not inherently democratic and human rights impacts are particularly important to consider in humanitarian and forced migration contexts. An international human rights law framework is particularly useful for codifying and recognising potential harms, because technology and its development is inherently global and transnational. More oversight and issue-specific accountability mechanisms are needed to safeguard fundamental rights of migrants such as freedom from discrimination, privacy rights and procedural justice safeguards such as the right to a fair decision-maker and the rights of appeal.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it