Person, Human, Neither: The Dehumanization Potential of Automated Image Tagging

Abstract

Following the literature on dehumanization via technology, we audit six proprietary image tagging algorithms (ITAs) for their potential to perpetuate dehumanization. We examine the ITAs' outputs on a controlled dataset of images depicting a diverse group of people for tags that indicate the presence of a human in the image. Through an analysis of the (mis)use of these tags, we find that there are some individuals whose 'humanness' is not recognized by an ITA, and that these individuals are often from marginalized social groups. Finally, we compare these findings with the use of the 'face' tag, which can be used for surveillance, revealing that people's faces are often recognized by an ITA even when their 'humanness' is not. Overall, we highlight the subtle ways in which ITAs may inflict widespread, disparate harm, and emphasize the importance of considering the social context of the resulting application.