‘Zizi - Queering the Dataset’ aims to tackle the lack of representation and diversity in the training datasets often used by facial recognition systems. The video was made by disrupting these systems and re-training them with the addition of 1000 images of drag and gender fluid faces found online. This causes the weights inside the neural network to shift away from the normative identities it was originally trained on and into a space of queerness. ‘Zizi - Queering The Dataset’ lets us peek inside the machine learning system and visualise what the neural network has (and hasn’t) learnt. The work is a celebration of difference and ambiguity, which invites us to reflect on bias in our data driven society.
- Director
- Jake Elwes
