期刊
SCIENCE TECHNOLOGY & HUMAN VALUES
卷 48, 期 3, 页码 663-689出版社
SAGE PUBLICATIONS INC
DOI: 10.1177/01622439211060839
关键词
artificial intelligence; statistics; India; biometrics; facial recognition; race; Mahalanobis
This article explores the history of the Mahalanobis Distance Function and its transition from colonial India to contemporary artificial intelligence technologies. The function was initially created to address the issues of caste and ethnic classification in colonial India but has since become a technical standard and racialized technique in machine learning applications.
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据