Deconstructing whiteness

Deconstructing Whiteness is an interactive AI performance. It examines the visibility of race in general, and 'whiteness' in particular, through the lens of AI. The performance reveals some underlying racial constructs which compose the technological visibility of race. The artist uses an off-the-shelf face recognition program to resist her own visibility as a 'white' person. By utilizing a performative behavior she slightly changes her facial expressions and her hair style. These actions modify the confidence level by which the machine recognizes her as 'White'. Face recognition algorithms are becoming increasingly prevalent in our environment. They are embedded in products and services we use on a daily basis. Recent studies demonstrate that many of these algorithms reflect social disparities and biases which may harshly impact people's lives. This is especially true for people from underrepresented groups. Scholar Paul Preciado claims that if machine vision algorithms can guess facets of our identity based on our external appearance, it is not because these facets are natural features to be read, it is simply because we are teaching our machines the language of techno-patriarchal binarism and racism. However, it is important to remember that these systems are not 'things-of-themselves'; there is no reason for them to be outside of our reach. We are able to intermingle with these systems so that we better understand the coupling between the information and our own bodies. This entanglement, as seen in the performance, reveals our own agency and ability to act. In Deconstructing Whiteness the 'White' and 'Non-white' dichotomy is ditched in favor of a flow of probabilities which are meant to resist, confuse and sabotage the machinic vision and its underlying structural racism. The performance is also a call for others to become curious regarding their own visibility and to pursue a similar exploration.