Zizi – Queering the Dataset by Jake Elwes aims to tackle the lack of representation and diversity in the training datasets often used by facial recognition systems. The prints are drawn from a process developed by disrupting these systems* and retraining them with the addition 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 works are a celebration of difference and ambiguity, inviting us to reflect on bias in our data-driven society.

*A Style-Based Generator Architecture for Generative Adversarial Networks (2019)