AHP readers may be interested in a new piece in Science, Technology, & Human Values: “Artificial Intelligence from Colonial India: Race, Statistics, and Facial Recognition in the Global South,”
Simon Michael Taylor, Kalervo N. Gulson, and Duncan McDuie-Ra. Abstract:
This article examines the history of a similarity measure—the Mahalanobis Distance Function—and its movement from colonial India into contemporary artificial intelligence technologies, including facial recognition, and its reapplication into postcolonial India. The article identifies how the creation of the Distance Function was connected to the colonial “problem” of caste and ethnic classification for British bureaucracy in 1920-1930s India. This article demonstrates that the Distance Function is a statistical method, originating to make anthropometric caste distinctions in India, that became both a technical standard and a mobile racialized technique, utilized in machine learning applications. The creation of the Distance Function as a measure of “similitude” at a particular period of colonial state-making helped to model wider categories of classification which have proliferated in facial recognition technology. Overall, we highlight how a measurement function that operates in recognition technologies today can be traced across time and space to other racialized contexts.