4.5 in x 7 in 15 b&w illus., 150 pages
Published by Sternberg Press.
The Black Technical Object
On Machine Learning and the Aspiration of Black Being The Antipolitical
by Ramon Amaro
- ISBN:
- 978-3-95679-563-3
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A contemplation on the abstruse nature of machine learning, mathematics, and the deep incursion of racial hierarchy.
https://mitpress.mit.edu/books/black-technical-object
The Black Technical Object aims at introducing the history of statistical analysis and a knowledge of sociogenesis—a system of racism amenable to scientific explanation—into machine learning research as an act of impairing the racial ordering of the world. While machine learning—computer programming designed for taxonomic patterning—provides useful insight into racism and racist behavior, a gap is present in the relationship between machine learning, the racial history of scientific explanation, and the Black lived experience.
Ramon Amaro explores how the history of data and statistical analysis provides a clear (and often sudden) grasp of the complex relationship between race and machine learning. Amaro juxtaposes a practical analysis of machine learning with a theory of Black alienation in order to inspire alternative approaches to contemporary algorithmic practice. In doing so, he offers a continuous …
A contemplation on the abstruse nature of machine learning, mathematics, and the deep incursion of racial hierarchy.
https://mitpress.mit.edu/books/black-technical-object
The Black Technical Object aims at introducing the history of statistical analysis and a knowledge of sociogenesis—a system of racism amenable to scientific explanation—into machine learning research as an act of impairing the racial ordering of the world. While machine learning—computer programming designed for taxonomic patterning—provides useful insight into racism and racist behavior, a gap is present in the relationship between machine learning, the racial history of scientific explanation, and the Black lived experience.
Ramon Amaro explores how the history of data and statistical analysis provides a clear (and often sudden) grasp of the complex relationship between race and machine learning. Amaro juxtaposes a practical analysis of machine learning with a theory of Black alienation in order to inspire alternative approaches to contemporary algorithmic practice. In doing so, he offers a continuous contemplation on the abstruse nature of machine learning, mathematics, and the deep incursion of racial hierarchy.