Person

Generative AI poses an increasingly big societal challenge as it replicates and amplifies societal biases and as the use of AI technologies increases, so do the ways AI influence the world. This often impacts marginalised groups negatively whose representations through AI systems are distorted, become stereotypical, are made invisible or are simply moderated out of results. This is the case for nonbinary and trans individuals.

My doctoral thesis aims to describe and analyse the activities, strategies and experiences of trans and nonbinary people when dealing with the challenges that prompting visual generative AI tools entail. The challenges they encounter opens up to an exploration of the space of friction that occurs between prompters' imaginaries and the systems' representational limitations when the expectations aren't met. Whose reality and representations takes precedence? What knowledge production is made possible through the enforced limitations of visual generative AI systems?