theory
When writers speak of viewing a problem through a statistical lens, the implication is often that a layer of objectivity is being added to the perception of an issue. However, many theorists (Adorno / Horkheimer, 1947; Bauman, 1989) have argued that this trend in modern society to deal in quantitative data has led to the dehumanization of the individual and the rationalization of evil.
Our concept will encourage viewers to reflect on the 'objective' nature of statistics and how it has impacted their perception of race. In doing so it will touch upon sensitive issues of racial stereotyping and how this stereotyping has affected our political and cultural landscape.
The statistics projected will be true, but will also be carefully hand-picked in order to reinforce commonly held racial stereotypes. For example, when the computer identifies an Asian one statistic that might be displayed is that Asians score 20-25% higher on standardized math tests than students of other races. For a Hispanic subject the statistics could describe the number of illegal immigrants in the US.
It is our hope that when the viewer is confronted with these statistics they will attempt to reconcile them with the individual that they are looking at. Often times this will be a person that will have accompanied them to the museum; by looking at the statistical profiles of friends and loved ones and rejecting the statistics in favor of their own personal experience we hope that viewers will consider the effects that statistics have played in any stereotypes they may hold and attempt to deconstruct those effects in the future.
In addition to looking at statistics specifically, our concept also examines imperfections in the methods we use to determine race and the incorrect stereotyping of individuals that may result from these flaws. It is often impossible to determine race and ethnicity via visual perception alone and yet our visual perception is often what shapes our initial impression of any given individual. As we incorrectly guess the race of an individual, the stereotypes that we hold about a particular race are brought into play and color our perception. When computer analysis yields what is perceived as an incorrect estimation of a subjects race, the viewer will have to analyze how they themselves determine the race of an individual in order to understand why they think the computer is wrong. Thus, incorrect estimations by the computer are not actually a cause of concern; instead they are another opportunity to invite reflection by the viewer wearing the glasses.