Accepted Paper:

The Navigators  


Nicholas Seaver (Tufts University)

Paper short abstract:

Data scientists construct and navigate data spaces. Where critical data studies has focused on flaws in these spaces' construction, this paper examines their navigation. Studies of navigation illuminate key features of data science, particularly the interrelation of maps, spaces, plans, and action.

Paper long abstract:

Data scientists summon space into existence. Through gestures in the air, visualizations on screen, and loops in code, they locate data in spaces amenable to navigation. Typically, these spaces embody a Euro-American common sense: things near each other are similar to each other. This principle is evident in the work of algorithmic recommendation, for instance, where users are imagined to navigate a landscape composed of items arranged by similarity. If you like this hill, you might like the adjacent valley. Yet the topographies conceived by data scientists also pose challenges to this spatial common sense. They are constantly reconfigured by new data and the whims of their minders, subject to dramatic tectonic shifts, and they can be more than 3-dimensional. In highly dimensional spaces, data scientists encounter the "curse of dimensionality," by which human intuitions about distance fail as dimensions accumulate. Work in critical data studies has conventionally focused on the biases that shape these spaces. In this paper, I propose that critical data studies should not only attend to how representative data spaces are, but also to the techniques data scientists use to navigate them. Drawing on fieldwork with the developers of algorithmic music recommender systems, I describe a set of navigational practices that negotiate with the shifting, biased topographies of data space. Recalling a classic archetype from STS and anthropology, these practices complicate the image of the data scientist as rationalizing, European map-maker, resembling more closely the situated interactions of the ideal-typical Micronesian navigator.

Panel T113
Critical data studies