Abstract
In this chapter, I discuss practices and technologies used to extract data from what (McAfee & Brynjolfsson, Harvard Business Review 90:61–67, 2012), in their seminal paper on big data, call “walking data generators.” I focus on data extractions (namely, practices that focus on maximizing the collection, storage, and processing of so-called “big data”) and data extractors (the technologies used to perform such tasks (extractions)).
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Other relevant contribution to the literature (and practice) of the dynamics between Uber drivers and the app were recently made by my colleague and friend Mareike Mohlmann, also coauthor of the paper that is cited above. Examples of her contributions are Möhlmann and Henfridsson (2019), Möhlmann (2021), and Möhlmann et al. (2021).
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Note that this system was created in 1965, but it was only in 199 that the system “targeted” the poor (http://etheses.lse.ac.uk/950/).
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Marabelli, M. (2024). Data Extractions and Extractors. In: AI, Ethics, and Discrimination in Business. Palgrave Studies in Equity, Diversity, Inclusion, and Indigenization in Business. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-031-53919-0_2
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