Ramon Amaro

Abstract

This paper suggests that we can usefully understand racism in machine learning algorithms by approaching the black body as a key site through which power and calculation regulate free activity. Machine learning could be described as the systematic study of algorithms that improve their knowledge based on the calculation of experience, orienting human behaviours into specific patterns that form and alter the boundaries between preempted acts and lived potential. The paper considers how machine learning functions as a threshold through which blackness enters, even unwittingly, into social, political and economic experience by the arrival of certain race specific characteristics in the production of operational forms of reason. Here, racism is seen as an error in algorithmic intentions or human judgement, orienting the machine as mediator of racialised experience. By placing blackness in dialogue with machine learning it is suggested that racism instead forms its own modes of assembly, where power lies at the convergence of the discreet black body and the modulation of mathematics. These sites also form cultural productions, where acts of freedom and alternative modes of living can be enacted.

Bio

Ramon Amaro is Associate Lecturer in Interactive Media: Critical Theory and Visual Cultures, and a PhD researcher at the Centre for Cultural Studies, Goldsmiths, University of London. He holds an advanced degree in Sociological Research, and a BSe in Mechanical Engineering. Prior to his doctoral research, Ramon was a programmes manager for the American Society of Mechanical Engineers and a quality engineer for General Motors Corporation. His research interests include machine learning, black study, social modelling and the philosophy of maths.