Adapting algorithms : how computational processes move between cultures

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2017-05

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Computational processes such as machine learning algorithms are useful in a variety of domains. While largely developed by computer scientists, they are applied in contexts from sports and marketing to law enforcement, as well as in a variety of academic areas such as the natural sciences, social sciences and humanities. The people working in these contexts often have different goals and values than do the computer scientists who develop computational processes—in the terms used here, they are of different cultures. As computational processes continue to spread to new contexts and the promotional rhetoric around big data and analytics encourages their use, it’s important to consider how they can support these diverse goals and values. To what extent does the movement of technical objects also entail the imposition of foreign goals and values? And how do people adapt technical objects in order to align them with their personal needs? In this dissertation, I highlight the diversity of cultures in which computational processes are used by focusing on the work of humanities scholars. As I show, these scholars use the same processes as do computer scientists, but they use them in novel ways. Drawing on interviews, observations and collaborative work with humanities scholars, I develop the concept of fitting practices as a way to connect the properties of computational processes with the goals and values of workers who use them. As I illustrate, prior work in Science and Technology Studies that addresses the adoption of new computational processes has primarily focused on the relationships between processes and other objects (such as software tools, companies and governments). While this work leads to analyses of how knowledge production and decision-making are constrained in specific historical periods, it does less to illuminate how computational processes might be used in new ways. I contribute to this literature by suggesting a more generative and future-oriented perspective on the use of computational processes that might also be applied to the use of technical objects, more broadly.

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