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The article proposes a reading strategy focused on the act of reading, its difficulties, and the ethico-politics they produce, addressing genre, readability, and meaning.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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\nLouise Amoore , Alexander Campolo , Benjamin Jacobsen  and Ludovico Rella   \nBig Data & Society January–June: 1–13  \n© The Author(s) 2023  \nArticle reuse guidelines: [sagepub.com/journals-permissions](sagepub.com/journals-permissions)[ ](sagepub.com/journals-permissions)[DOI: 10.1177/20539517231166887](DOI: 10.1177/20539517231166887)[ ](DOI: 10.1177/20539517231166887)[journals.sagepub.com/home/bds](journals.sagepub.com/home/bds)  \nAbstract  \nComputer 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 isnot 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.  \nKeywords  \nmachine learning, neural networks, ethics, reading, politics, computer science  \nReading, if it happens at all, happens only in the encounter with difficulty and without guarantees (Keenan, 1994: 103) .  \nIn a series of interviews recorded in 2018, Turing laureates Geoffrey Hinton and Yann LeCun reflect on the breakthrough moments that shaped contemporary machine learning and artificial intelligence. Discussing the development of neural network algorithms, they describe how their work on backpropagation and convolutional neural networks had initially been rejected by the computer science community. “They rejected papers by Yann, even though they worked better on particular problems,” Hinton remarks, because “the referees thought it was the wrong way to do things”(Ford, 2018: 76) .“The general consensus among statisticians and people in AI was that we were wishful thinkers,” Hinton continues, “who thought that just from the inputs and outputs you should be able to learn all these weights”(2018: 76) . LeCun similarly comments on the difficulty of publishing on neural networks in the 1980s when it was “anathema within the community.  \nYou couldn’t publish a paper that even mentioned the phrase neural networks because it would immediately be rejected by your peers” (2018: 122) . In the accounts of Hinton and LeCun, the published computer science paper does not primarily reveal the state-of-the-art in machine learning, but rather it actively frames what can and cannot be accepted as knowledge in a machine learning community.  \nThe computer science text – and whether and how it is read by others – is a lively and contested site through which machine learning shapes the world. When work is rejected by a major computer science conference, the failure of a paper to make it out into the world is said to obscure what is “really going on” in the field. As LeCun recounts,“Geoffrey Hinton and Terry Sejn","cbCaievxl5S1iW4d","https://ap.wps.com/l/cbCaievxl5S1iW4d","pdf",919716,13,"English","# Abstract\n# Keywords\n## Reading strategies and the difficulty of meaning-making\n## Three reading problems: genre, readability, meaning\n## Machine learning texts as contested sites","[{\"question\":\"How does the article characterize typical barriers to reading computer science texts?\",\"answer\":\"It argues that computer science tends to foreclose reading by social science and humanities scholars through code and scale, mathematics, and black-box opacities or proprietary models.\"},{\"question\":\"What is the proposed reading strategy for computer science papers?\",\"answer\":\"The strategy is attentive to the act of reading itself, stays close to the difficulty of reading, and works with the text as shaped by the ethico-politics engendered by that difficulty.\"},{\"question\":\"Which three “reading problems” does the article address?\",\"answer\":\"It addresses genre, readability, and meaning as a series of three reading problems for how machine learning texts are engaged with.\"}]","Machine learning, meaning making - On reading computer science texts | PDF",33]