Visions of vectors: sense, race, and colonialism in machine learning practice

Abstract

ABSTRACT This paper interrogates the informational practices shared between human and computer machine learners as they train to sense the world through lines of order, or vectors. The paper does this by exploring the affective conditions through which vectors draw relations of data over a persistent, colonial image of race. Through analysis of pedagogical practices at the Summer Institute for Computational Social Science in Chicago, and a corresponding year-long machine learning design group, this paper examines how contemporary machine learning practitioners train themselves to sense calculative relationality on the basis of racialized difference. The paper compares this vectorized sensibility with 20th century enumerative practices in the United States by analyzing the racial statistics of W.E.B. Du Bois, Kelly Miller, and Frances Kellor to trace out affective histories of the vector. Ultimately, this paper asks how machine learners – whether algorithms or their human users – often project lines of colonial order upon other forms of life, and how, by questioning the claim of vector relations and their informational objects, we can confront this sense-training and reimagine ourselves.