Notice bibliographique
Résumé
Border wall between US and Mexico, February 2022, Petra Molnar Like a wound in the landscape, the rusty border wall cuts along Arizona''s El Camino Del Diablo, the Devil's Highway. Once the pride and joy of the Trump Administration, this wall is once again the epicentre of a growing political row. President Biden's May 2023 repeal of the Trump Administration's Covid-era Title 42 regulation comes with the introduction of hardline new policies preventing people from claiming asylum in the United States, undergirded by a growing commitment to a virtual smart border extending far beyond the physical frontier (Transnational Institute, 2023). Racism, technology, and borders create a cruel intersection (Achiume, 2020). From unpiloted drones used to prevent people from reaching the safety of European shores (Statewatch, 2021), to artificial intelligence (AI) lie detectors at various airports worldwide (Gallagher & Jona, 2019), to robodogs patrolling the US-Mexico border (DHS, 2022), people on the move are caught in the crosshairs of an unregulated and harmful set of technologies. These projects are touted to control migration, bolstering a multi-billion-dollar border industrial complex (Akkerman, 2021). Coupled with increasing international environmental destabilization, more and more people are ensnared in a growing and global surveillance dragnet. Thousands have already died (Boyce & Chambers, 2022). The rest experience old and new traumas provoked and compounded by omnipresent surveillance and automation (Molnar, 2024). Every point in a person''s migration journey now involves some level of digitalization and techno-solutionism. Decisions on whether to grant a visa or detain someone, which would otherwise be made by administrative tribunals, immigration officers, or border agents are now made through algorithmic decision-making (Molnar & Gill, 2018). Analytics which use large data sets to make predictions about human behaviour are used both in humanitarian emergencies to deliver aid but also to see where people may be crossing borders (Monroy, 2021). Refugee camps and spaces of humanitarian emergency are also seeing the rise in the use of biometrics, or the automated recognition of individuals based on their biological and behavioural characteristics (Robbin, 2022). Biometrics can include fingerprint data, retinal scans, and facial recognition, as well as less well-known methods such as the recognition of a person''s vein and blood vessel patterns, ear shape, and even gait. Even more experimental are lie detectors relying on AI deciding who is telling the truth at the border (Bacchi, 2022), while voice printing technologies analyse accents and pattern of speech in German refugee applications (Ozkul, 2023). The surveillance dragnet is also expanding, with a growing arsenal of cameras, blimps, loud sound cannons, and even experimental robo-dogs deployed to control borders (Molnar & Miller, 2022). Beyond the fortification of borders, states also often justify the need to develop and deploy this technology to increase efficiency and reduce wait times for various immigration and refugee applications (Molnar, 2021). But these types of decisions are notoriously complex. Two human officers looking at the same set of evidence can make two completely different yet equally legally valid determinations, making different determinations about complex and opaque factors such as a person's credibility or how much weight to give various factual underpinnings of their case. How will then an automated system be able to deal with the nuances of people's applications? And what happens when an algorithm makes a mistake? An algorithm already wrongfully deported over 7,000 students from the UK after accusing them of cheating on a language acquisition test (Baynes, 2019). Unfortunately, liability can be difficult to pin down: Does it lie with the designer, the coder, the immigration officer, or the algorithm itself? How will judges parse out where automated decision end and human decision- making begins, when automation bias, or our predisposition to consider algorithms'’ decisions to be more objective and truthful, begins to colour how human officers make decisions? (Molnar, 2020). Much of immigration and refugee decision-making already sits at an uncomfortable legal nexus: the impact on the rights of individuals is significant, even where procedural safeguards are weak (Molnar & Gill, 2018). When a portion of—or an entire decision—is made by an algorithm, a whole new system of administrative decision-making emerges. These automations will impact how courts interpret algorithmic decision-making and how to apply relevant administrative law principles like procedural fairness, the right to an impartial decision-maker, and our rights of appeal when mistakes are made. Refugee claims and immigration applications are filled with nuance and complexity (Evans Cameron, 2018), qualities that may be lost on automated technologies, leading to serious breaches of internationally and domestically protected human rights in the form of bias, discrimination, privacy breaches, and due process and procedural fairness issues, among others (Molnar, 2019). The use of technological tools once again also raises concerns about information sharing without people''s consent, as well as about racism, bias, and discrimination, as various tools such as facial recognition technologies struggle when analysing women or people with darker skin tones (Achiume, 2020). This concerning trend seems to be to push for innovation even in high-risk areas where very little regulation and oversight exists over the experimental use of technology, such as the situation on various Greek islands, where high-tech refugee camps full of biometric data collection, automatic surveillance, and even virtual reality glasses for the guards are being rolled out (Fallon and Emmanouilidou 2021). While promising new governance mechanisms like the EU''s proposed Act to Regulate Artificial Intelligence are emerging, these laws do not go far enough to recognize the very real harms of high-risk border technologies (EDRi et al., 2023). Kos Refugee Camp, one of five high-tech Multi-Purpose Reception and Identification Centers funded by the European Union, December 2021, Petra Molnar Developing and deploying these tools also has a normative power, normalizing certain technological interventions over others, often pushed by the private sector as viable solutions to border enforcement. For example, while more resources could be spent of on utilizing innovative technologies to root out racism at the border, digital tools are once again weaponized against marginalized communities in the forms of surveillance, AI-lie detectors, and robo-dogs (Molnar, 2020). Instead of thinking about ways in which to improve access to justice and psychological support, people are presupposed to be criminals and terrorists out to cheat the system justifying massive investment into increasingly more draconian solutions. This increasingly global and lucrative panopticon of migration control exacerbates discrimination and obfuscates responsibility and liability through the development and deployment of increasingly hardline border technologies (Achiume, 2020). Technological experiments play up the ‘us’ verus ‘them’ mentality at the centre of migration management policy. Unbridled techno-solutionism (Molnar & Naranjo, 2020) exacerbates deterrence mechanisms already so deeply embedded in the global migration management strategy, making migration as difficult as possible in order to set an example and to prevent others from coming. But what is the logic underpinning these technological border logics and the spectacles of increasingly higher risk border technologies? As writer Harsha Walia argues, racist nationalism, border imperialism and privatization of migration management are at the heart of border control: Wealth is allowed to flow to the centers of global capital but never outward. The mobility of capital never applies to the wretched of the earth, who are forced to traverse deadly water and land passages across borders, not of their making, and are unwelcome in countries that may have destroyed theirs. Only punishment awaits them. Their mobility is made criminal, their existence made illegal. While it is framed as a migrant or refugee crisis, it is really a crisis of humanity, the failure of the current system to offer any real alternative other than the demonization of the other. (Walia, 2020) Whether retinal scans or AI lie detectors at the airport, the primary purpose of these projects is to collect data, make decisions, and report to the state the necessary information on a potentially unsafe or unknown migrant body, rendering them into security objects and data points to be analysed, stored, collected, and rendered intelligible (Beduschi, 2020; Molnar, 2019). All these technological experiments deliberately occur in a space that is largely unregulated, with weak oversight and governance mechanisms, driven by the private sector innovation (see also Aradau 2023 in this volume). They need to be situated in a broader and historical system of control, bolstering a lucrative industry where private sector players set the agenda without virtually any meaningful governance and oversight mechanisms (Molnar, 2021). The creation of such legal black holes without accountability and oversight is deliberate to allow for the creation of opaque zones of technological experimentation that simply would not be allowed to occur in other spaces the same way (Molnar, 2022), like a local grocery store or doctor''s office. The use of border technologies reinscribes the way that powerful actors make decisions that affect thousands of people on the margins of society. However, the allure of technological interventions at and around the border has very real impacts on people''s lives, exacerbated by a deliberate lack of meaningful governance and oversight mechanisms of these technological experiments, undergirded by an interconnected systems of power, history, politics, and economics. Memorial Site for Mr. Alvarado, who died in the surveillance dragnet of the Sonora Desert, February 2022, Petra Molnar In total, 77 border walls and counting (Vallet, 2022) are now cutting the landscape of the world, like gaping wounds. These wounds are both physical and digital, a growing panopticon of exclusion where people on the move are once again caught at the sharpest edges. The opinions expressed in this Commentary are those of the author and do not necessarily reflect the views of the Editors, Editorial Board, International Organization for Migration nor John Wiley & Sons.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».