People who know very little about technology seem to attribute an aura of “objectivity” and “impartiality” to Big Data and analyses based on them. Statistics and predictive analytics give the impression, to the outside observer, of being able to reach objective conclusions based on massive samples. But why exactly is that so? How has it come that a societal discourse has ascribed that certain aura to Big Data analyses?
Since most people conceive of Big Data as tables filled with numbers which have been collected by machines observing human behavior, there are at least two points intermingled in this peculiar aura of Big Data: The belief that numbers are impartial and preinterpretive, and the conviction that there exists something like mechanical objectivity. Both concepts have a long history, and it is therefore wise to consult cultural historians and historians of science.
With respect to the claim that numbers are theory-free and value-free, one can consult the book “A History of the Modern Fact” by Mary Poovey. Poovey traces the history of that modern epistemological assumption that numbers are free of an interpretive dimension, and she points to the story of how description came to seem separate from interpretation. In analyzing historical debates about induction and by studying authors such as Adam Smith, Thomas Malthus, and William Petty, Poovey points out that “Separating numbers from interpretive narrative, that is, reinforced the assumption that numbers were different in kind from the analytic accounts that accompanied them.” (XV) If nowadays many members of our societies imagine that observation can be separated from analysis and numbers guarantee value-free description, this is the result of the long historical process examined by Poovey. But seen from an epistemological point this is not correct, because numbers are interpretive – they embody theoretical assumptions about what should be counted, they depend on categories, entities and units of measurement established before counting has begun, and they contain assumptions on how one should understand material reality.
The second point, mechanical objectivity, has been treated by Lorraine Daston and Peter Galison in their book on “Objectivity”; it contains a chapter of the same name. Daston and Galison focus on photography as a primary metaphor for the objectivity ascribed to a machine. Alongside this example, they describe mechanical objectivity as “the insistent drive to repress the willful intervention of the artist-author, and to put in its stead a set of procedures that would, as it were, move nature to the page through a strict protocol, if not automatically.” (121) Both authors see two intertwined processes at work: On the one hand the separation of the development and activities of machines from the human beings who conceived them, with the result that machines were attributed freedom from the willful interventions that had come to be seen as the most dangerous aspects of subjectivity. And on the other hand the development of an ethics of objectivity, which called for a morality of self-restraint in order to refrain researchers from intervention and interferences like interpretation, aestheticization, and theoretical overreaching. Thus machines – be they cameras, sensors or electronic devices – have become emblematic for the elimination of human agency.
If the aura of Big Data is based on these conceptions of an “impartiality” of numbers and data collected by “objectively” working machines, there remains little space for human agency. But this aura proves of a false consciousness, the consequences of which can easily be seen: If analyses based on Big Data are taken as ground truth, it is no wonder that there is no space being opened up for a public discussion, for decisions made independently by citizens, and for a democratically organized politics, where the processes in which Big Data play an important role are being shaped actively.
 Mary Poovey, A History of the Modern Fact. Problems of Knowledge in the Sciences of Wealth and Society, Chicago / London: The University of Chicago Press 1998.
 Lorraine Daston, Peter Galison, Objectivity, New York: Zone Books 2007.
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