Article by Rida Qadri, Michael Madaio, and Mary L. Gray: “Imagine you are a marketing professional prompting an artificial intelligence (AI) image generator to produce different images of Pakistani urban streetscapes. What if the model, despite the prompting for specificity, produces Orientalist scene after scene of dusty streets, poverty, and chaos—missing important landmarks, social scenes, and the human diversity that makes a Pakistani city unique? This example illustrates a growing concern with the cultural inclusivity of AI systems failing to work for global populations, but instead, reinforce stereotypes that erase swaths of particular populations in AI-generated output.
To address such issues of cultural inclusion in AI, the field has attempted to incorporate cultural knowledge into models through a common tool in its arsenal: datasets. Datasets of, for instance, global values, offensive terms, and cultural artifacts are all attempts to incorporate cultural awareness into models.
But trying to capture culture in datasets is akin to believing you have captured everything important about the world in a map. A map is an abstracted and simplified two-dimensional representation of a multidimensional world. While a valuable tool, using maps effectively requires understanding the limits of their correspondence with the physical world. One must know, for example, how the Mercator projection map, created in the 1500s and adopted in the 1700s as the global standard for navigation, distorted the relative sizes of the continents. Confusing the abstraction for the reality has led to all sorts of trouble. Colonial powers used the Mercator projection maps of the physical world to demarcate social worlds—drawing lines through simplified representations on a map, separating communities and leading to decades of ethnic strife, all to make navigation supposedly more efficient…(More)”.
