Technologies and Asylum Procedures

After the COVID-19 pandemic stopped many asylum procedures throughout Europe, fresh technologies have become reviving these kinds of systems. Coming from lie diagnosis tools analyzed at the border to a system for confirming documents and transcribes interviews, a wide range of technology is being employed in asylum applications. This article is exploring how these technology have reshaped the ways asylum procedures are conducted. This reveals just how asylum seekers are transformed into pressured hindered techno-users: They are asked to conform to a series asylum procedure advice of techno-bureaucratic steps also to keep up with unstable tiny changes in criteria and deadlines. This obstructs their very own capacity to browse through these systems and to follow their right for safety.

It also demonstrates how these types of technologies are embedded in refugee governance: They help the ‘circuits of financial-humanitarianism’ that function through a whirlwind of dispersed technological requirements. These requirements increase asylum seekers’ socio-legal precarity by hindering these people from being able to access the stations of proper protection. It further argues that analyses of securitization and victimization should be combined with an insight into the disciplinary mechanisms of them technologies, through which migrants are turned into data-generating subjects just who are regimented by their reliability on technology.

Drawing on Foucault’s notion of power/knowledge and comarcal expertise, the article states that these technologies have an natural obstructiveness. They have a double impact: even though they assistance to expedite the asylum process, they also produce it difficult to get refugees to navigate these kinds of systems. They are positioned in a ‘knowledge deficit’ that makes these people vulnerable to illegitimate decisions made by non-governmental actors, and ill-informed and unreliable narratives about their situations. Moreover, they will pose new risks of’machine mistakes’ that may result in inaccurate or discriminatory outcomes.