‘Zizi - Queering the Dataset’ 2019 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 systems1 and re-training them with the addition of drag and gender fluid faces found online.
Monitor Computer led
Zizi - Queering the Dataset
2019, multi-channel digital video, 135 minute loop
‘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 systems1 and re-training 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 work is a celebration of difference and ambiguity, which invites us to reflect on bias in our data driven society.
The Zizi Project is a collection of works by Jake Elwes exploring the intersection of Artificial Intelligence (A.I.) and drag performance. Drag challenges gender and explores otherness, while A.I. is often mystified as a tool and contains social bias. Zizi combines them through a deep fake, synthesised drag identity created using machine learning. The project explores what AI can teach us about drag, and what drag can teach us about A.I.
1) A Style-Based Generator Architecture for Generative Adversarial Networks (2019)
Read full curatorial text by Drew Hemment, Edinburgh Futures Institute.
Instagram @zizidrag - machine learning generated captions trained on drag profiles.
Zizi was originally commissioned as a seven channel video installation by Experiential AI at Edinburgh Futures Institute and Inspace, The University of Edinburgh.
Presented as site specific video installation with between 3 and 10 projected video channels.