the always-present difficulty of legibility between words and seeing.[3]
This is a thing with prompting for images - how do we approach un/commonality of language. Can we understand weighting of models and adjust language or is it fixed, can we make it malleable if we don’t know what we are adjusting for?
Knowledge is now produced in relation to planetary computational infrastructures in which other agents such as algorithms generalise massive amounts of (big) data.[6
“machine learning” techniques are employed on data to produce forms of knowledge that are inextricably bound to hegemonic systems of power and prejudice
Adrian Mackenzie, “The Production of Prediction: What Does Machine Learning Want?,” European Journal of Cultural Studies, 18, 4–5 (2015): 431. [9] Mackenzie, “The Production of Prediction,” 441
10] See, for instance, Antoinette Rouvroy’s “Technology, Virtuality and Utopia: Governmentality in an Age of Autonomic Computing,” in The Philosophy of Law Meets the Philosophy of Technology: Computing and Transformations of Human Agency, eds. Mireille Hildebrandt and Antoinette Rouvroy (London: Routledge, 2011), 136–157.
10] See, for instance, Antoinette Rouvroy’s “Technology, Virtuality and Utopia: Governmentality in an Age of Autonomic Computing,” in The Philosophy of Law Meets the Philosophy of Technology: Computing and Transformations of Human Agency, eds. Mireille Hildebrandt and Antoinette Rouvroy (London: Routledge, 2011), 136–157.