Skip to main content

Research Repository

Advanced Search

Machine learning, meaning making: On reading computer science texts

Amoore, Louise; Campolo, Alexander; Jacobsen, Benjamin; Rella, Ludovico

Machine learning, meaning making: On reading computer science texts Thumbnail



Computer science tends to foreclose the reading of its texts by social science and humanities scholars – via code and scale, mathematics, black box opacities, secret or proprietary models. Yet, when computer science papers are read in order to better understand what machine learning means for societies, a form of reading is brought to bear that is not primarily about excavating the hidden meaning of a text or exposing underlying truths about science. Not strictly reading to make sense or to discern definitive meaning of computer science texts, reading is an engagement with the sense-making and meaning-making that takes place. We propose a strategy for reading computer science that is attentive to the act of reading itself, that stays close to the difficulty involved in all forms of reading, and that works with the text as already properly belonging to the ethico-politics that this difficulty engenders. Addressing a series of three “reading problems” – genre, readability, and meaning – we discuss machine learning textbooks and papers as sites where today's algorithmic models are actively giving accounts of their paradigmatic worldview. Much more than matters of technical definition or proof of concept, texts are sites where concepts are forged and contested. In our times, when the political application of AI and machine learning is so commonly geared to settle or predict difficult societal problems in advance, a reading strategy must open the gaps and difficulties of that which cannot be settled or resolved.


Amoore, L., Campolo, A., Jacobsen, B., & Rella, L. (2023). Machine learning, meaning making: On reading computer science texts. Big Data and Society, 10(1),

Journal Article Type Article
Acceptance Date Mar 11, 2023
Online Publication Date Mar 30, 2023
Publication Date 2023
Deposit Date Apr 20, 2023
Publicly Available Date Apr 20, 2023
Journal Big Data & Society
Electronic ISSN 2053-9517
Publisher SAGE Publications
Peer Reviewed Peer Reviewed
Volume 10
Issue 1


Published Journal Article (920 Kb)

Publisher Licence URL

Copyright Statement
This article is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 License ( which permits non-commercial use, reproduction and distribution of the work as published without adaptation or alteration, without further permission provided the original work is attributed as specified on the SAGE and Open Access page (

You might also like

Downloadable Citations