Nurturing Ab/Normal Bodies and Speculative Futures: How AI Mistakes Somebody’s Hair for a Bomb

Abstract

Airports have long served as laboratories for new technological and social control strategies, where surveillance is accepted in exchange for security. With a history of discrimination against minorities, the airport full-body scanner is a controversial apparatus of social control in which bodies are undressed and scrutinized. Individuals whose bodies and behaviours deviate from established standards of normality re-emerge in these settings as suspected terrorists. A crowdsourced competition sponsored by the US Department of Homeland Security offered US$1.5 million to solve this problem using Artificial Intelligence (AI). While stakeholders considered it a success, the competition did not produce the expected results, exacerbating the problem due to an issue the developers dubbed “the Bob Marley lookalike guy problem,” which caused the AI to identify bombs on body parts deemed “abnormal” to the Western-centric training data. This challenge illustrates how the assumptions of normality assigned to AI training sets reproduce stereotypes and perpetuate discrimination against minorities. The paper discusses how the competition was organized, the social biases inherent in the solutions, and the developers’ oversight in considering the cultural aspects ingrained in the training data. I argue that AI generates spurious correlations that become historical inevitabilities, reinforcing existing social, economic, and political norms.