What can AI see? The image of the ‘migrant’ in the era of AI post-visualization

Abstract

ABSTRACT Over the past decade, artificial intelligence (AI) has become omnipresent in migration control and mobility surveillance, with AI systems now deployed across all aspects of migration management. Critics of such trends typically examine questions of ethics and rights from the vantage point of regulatory mechanisms and the limited venues for the redress of grievances. But if legal frameworks are as of yet forthcoming and do not necessarily apply to migrants, are there alternative mechanisms to critique algorithmic decision making? To explore this and related questions, this paper engages one such alternative by taking a ‘visual turn.’ In asking ‘what can AI see’ the paper interrogates the role of images in constructing AI's capacity to both understand migration and make appropriate decisions about migrants. In addition, a visual turn allows for exploration of an emergent age of post-visualization: a phenomenon whereby the values and meanings of what we see will be increasingly imparted to us by AI systems. The paper examines what AI sees with the help of an experiment, prompting an AI generative platform to draw distinctions between migrants, refugees, and people.