Perceiving justice in the AI city: Global evidence from an eye-tracking study of autonomous mobility

AI increasingly shapes urban mobility, yet its social justice implications remain poorly understood, especially across cultural contexts. This study examines how people perceive justice in AI-based mobility systems through visual attention and cognitive framing. Combining large-scale online eye-tracking (n = 1,272) with survey data from 22 cities worldwide, we analyse how individuals attend to and interpret social diversity across autonomous (AV) and human-driven contexts. Three key patterns emerge. First, visual attention to social categories such as nationality or income is structured and transferable across technological settings. Second, attentional variation reflects both individual (age, gender, AI experience) and contextual (regional, cultural) factors. Third, Bayesian modelling shows that justice perceptions depend less on visual engagement than on affective trust and perceived safety. Together, these findings reveal how automation reshapes cognitive-ethical orientations toward justice, illustrating that equitable AI mobility depends as much on perceptual inclusion and social recognition as on algorithmic fairness.