The Myth of Objective Data

Article by Melanie Feinberg: “The notion that human judgment pollutes scientific attempts to understand natural phenomena as they really are may seem like a stable and uncontroversial value. However, as Lorraine Daston and Peter Galison have established, objectivity is a fairly recent historical development.

In Daston and Galison’s account, which focuses on scientific visualization, objectivity arose in the 19th century, congruent with the development of photography. Before photography, scientific illustration attempted to portray an ideal exemplar rather than an actually existing specimen. In other words, instead of drawing a realistic portrait of an individual fruit fly — which has unique, idiosyncratic characteristics — an 18th-century scientific illustrator drew an ideal fruit fly. This ideal representation would better portray average fruit fly characteristics, even as no actual fruit fly is ever perfectly average.

With the advent of photography, drawings of ideal types began to lose favor. The machinic eye of the lens was seen as enabling nature to speak for itself, providing access to a truer, more objective reality than the human eye of the illustrator. Daston and Galison emphasize, however, that this initial confidence in the pure eye of the machine was swiftly undermined. Scientists soon realized that photographic devices introduce their own distortions into the images that they produce, and that no eye provides an unmediated view onto nature. From the perspective of scientific visualization, the idea that machines allow us to see true has long been outmoded. In everyday discourse, however, there is a continuing tendency to characterize the objective as that which speaks for itself without the interference of human perception, interpretation, judgment, and so on.

This everyday definition of objectivity particularly affects our understanding of data collection. If in our daily lives we tend to overlook the diverse, situationally textured sense-making actions that information seekers, conversation listeners, and other recipients of communicative acts perform to make automated information systems function, we are even less likely to acknowledge and value the interpretive work of data collectors, even as these actions create the conditions of possibility upon which data analysis can operate…(More)”.