Policy discussions and corporate strategies on machine learning are increasingly championing data reuse as a key element in digital transformations. These aspirations are often coupled with a focus on responsibility, ethics and transparency, as well as emergent forms of regulation that seek to set demands for corporate conduct and the protection of civic rights. And the Protective measures include methods of traceability and assessments of ‘good’ and ‘bad’ datasets and algorithms that are considered to be traceable, stable and contained. However, (...) these ways of thinking about both technology and ethics obscure a fundamental issue, namely that machine learning systems entangle data, algorithms and more-than-human environments in ways that challenge a well-defined separation. This article investigates the fundamental fallacy of most data reuse strategies as well as their regulation and mitigation strategies that data can somehow be followed, contained and controlled in machine learning processes. Instead, the article argues that we need to understand the reuse of data as an inherently entangled phenomenon. To examine this tension between the discursive regimes and the realities of data reuse, we advance the notion of reuse entanglements as an analytical lens. The main contribution of the article is the conceptualization of reuse that places entanglements at its core and the articulation of its relevance using empirical illustrations. This is important, we argue, for our understanding of the nature of data and algorithms, for the practical uses of data and algorithms and our attitudes regarding ethics, responsibility and regulation. (shrink)

How did the 3.5-inch Winchester hard disk drive become the fundamental building block of the modern data center? In attempting to answer this question, I theorize the concept of "data peripheries" to attend to the awkward, uneven, and unintended outsides of data infrastructures. I explore the concept of data peripheries by first situating Big Data in one of its many unintended outsides—an unassuming dog kennel in Indiana housed in a former permanent magnet manufacturing plant. From the perspective of this dog (...) kennel, I then build a history of the 3.5-inch Winchester hard disk drive, and weave this hard drive history through the industrial histories of rare earth mining and permanent magnet manufacturing, focusing principally on Magnequench, a former General Motors subsidiary, and its sale and movement of operations from Indiana to China in the mid-1990s and early 2000s. I then discuss how mobilities of rare earths, both as materials and political discourse, shape Big Data futures, and conclude by speculating on how using the situated lenses of data peripheries can open up new methods for studying the material entanglements of Big Data writ large. (shrink)

Social media commentary, satellite imagery and GPS data are a part of ‘alternative data’, that is, data that originate outside of the standard repertoire of market data but are considered useful for predicting stock prices, detecting different risk exposures and discovering new price movement indicators. With the availability of sophisticated machine-learning analytics tools, alternative data are gaining traction within the investment management and algorithmic trading industries. Drawing on interviews with people working in investment management and algorithmic trading firms utilizing alternative (...) data, as well as firms providing and sourcing such data, we emphasize social media-based sentiment analytics as one manifestation of how alternative data are deployed for stock price prediction purposes. This demonstrates both how sentiment analytics are developed and subsequently utilized by investment management firms. We argue that ‘alternative data’ are an open-ended placeholder for every data source potentially relevant for investment management purposes and harnessing these disparate data sources requires certain standardization efforts by different market participants. Besides showing how market participants understand and use alternative data, we demonstrate that alternative data often undergo processes of prospecting and assetization. We further contend that the widespread embracement of alternative data in investment management and trading encourages a financialization process at the data level which raises new governance issues. (shrink)