The production of prediction: What does machine learning want?

By Adrian Mackenzie. 2015

209 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

Locative media and data-driven computing experiments (2016, methodology): “Treating computing experiments as objects, we draw upon the anthropological tradition of recognising the agency of objects to further examine the critical steps through which locative data and computing experiments are made fit for each other (Kelty and Landecker, 2009; Mackenzie, 2015).”
Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability (2018, background): “…and Möllers, 2016: 1)—but also as relationships without social articulation (Eslami et al., 2015), elite structuring masked as emergent sociality (Mackenzie, 2015; Striphas, 2015), automated media circulation (Hallinan and Striphas, 2014; Napoli, 2014), optimized labor (Kushner, 2013) and…”
People’s Councils for Ethical Machine Learning (2018, background): “…human or the fact that it is “already public” or covered by some Terms of Service not only makes it difficult to see it as having ethical problems (what Markham & Buchanan, 2015 call the “distance principle”) but also puts it outside the purview of most institutional review boards (Leetaru, 2016).”
Algorithms and their others: Algorithmic culture in context (2016, background): “As studies by Mackenzie (2015), Neyland (2016) and Seaver (2015) begin to show, algorithms may beneﬁt from a similar approach.”
Machine learning, social learning and the governance of self-driving cars (2018, background): “This has led some to call for a ‘right to explanation’ (Goodman and Flaxman, 2016) in cases where the ‘production of prediction’ (Mackenzie, 2015) has profound consequences for people’s lives.”
Scrutinizing an algorithmic technique: the Bayes classifier as interested reading of reality (2017, background): “While I agree with Mackenzie (2015, p. 436) that it is not essential to have a detailed grasp of how data mining methods work, I believe that an understanding on the more general level of algorithmic techniques is both attainable and necessary.”