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.
AI methods and ubiquitous data sensors have enabled a new algorithmic quantification of affect with the possibility to detect or verify users’ identities, characteristics, emotional states, and physical traits. By scrutinizing how transient datasets are produced by user applied pressure on touch-screens (via fingertip commands) this paper showcases how sensory technology creeps into users’ everyday life with potential implementations connected to a series of emerging data issues engineered by a black-box design: one which obfuscates data production and precludes user consent under the disguise of “non-intrusive” features. Thereby, this paper explores the limits of user-based interrogation of black-boxes by researching tactile modes of operation, as a subset of behavioural biometrics, and sensors that register force in touch analysis and haptic technologies.
Presenting a citation analysis of biometric techniques around the proposed usage of pressure; the authors offer a case-study examination of zinc-based force-sensing materials that are cost-effective and scalable to ubiquitous-computing and a prototype developed using ‘each pixel as a sensor’. By combining these approaches, this paper argues that such developments constitute a phenomenological shift away from users’ perception to data infrastructures working as assemblages of hidden technical sensations, and there is a need to expose these complex networks to afford some grasp, if not direct agency, over their micro temporal operation. This work aims not simply to theorise, but to help reveal ways users may revise, embrace, resist, subvert or even live data practices that operate unlike conventional data harvesting techniques.
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