Misidentification in AI: Framing the face in-between machine-classified & self-perceived gender

The use of automatic face analysis is rapidly spreading in our society. This technology, like facial recognition, is primarily used for security and law enforcement purposes, but it is now becoming popular in other areas, like in recruitment, education and analysis of facial expression. However, facial recognition systems are consistently built on a gender binary construct and almost never take into account individuals who identify non-binary. As a consequence, these types of human-machine interfaces reinforce existing prejudices about these communities. By considering essential questions about the conditions under which digitalization creates knowledge and identities, our hypothesis is that in facial recognition systems non-binary databases are missing. In light of these issues, the goal of the activity is to provide a focused venue to discuss research challenges, concerns and solutions associated with building inclusive Facial Recognition systems, through a critical analysis on the relationships among gender, identity and face recognition technologies. The aim is twofold: i) encourage an interdisciplinary discussion on non-binary identities and face recognition technologies, to foster the development of inclusive, diverse and trustworthy AI; ii) highlight the dichotomy between self-perceived gender vs machine-classified gender.