After the COVID-19 pandemic halted many asylum procedures throughout Europe, fresh technologies are actually reviving these types of systems. By lie recognition tools examined at the boundary to a system for validating documents and transcribes interviews, a wide range of systems is being utilized for asylum applications. This article is exploring find more just how these systems have reshaped the ways asylum procedures will be conducted. That reveals just how asylum seekers will be transformed into obligated hindered techno-users: They are asked to abide by a series of techno-bureaucratic steps and also to keep up with unpredictable tiny within criteria and deadlines. This obstructs the capacity to browse through these systems and to go after their legal right for safeguards.
It also demonstrates how these kinds of technologies happen to be embedded in refugee governance: They aid the ‘circuits of financial-humanitarianism’ that function through a whirlwind of spread technological requirements. These requirements increase asylum seekers’ socio-legal precarity by hindering them from interacting with the channels of safeguard. It further states that analyses of securitization and victimization should be along with an insight into the disciplinary mechanisms for these technologies, by which migrants are turned into data-generating subjects so, who are self-disciplined by their reliance on technology.
Drawing on Foucault’s notion of power/knowledge and comarcal know-how, the article argues that these solutions have an natural obstructiveness. They have a double effect: even though they help to expedite the asylum procedure, they also produce it difficult for refugees to navigate these kinds of systems. They are really positioned in a ‘knowledge deficit’ that makes these people vulnerable to illegitimate decisions created by non-governmental stars, and ill-informed and unreliable narratives about their situations. Moreover, they pose new risks of’machine mistakes’ that may result in inaccurate or discriminatory outcomes.
