Zizi - Queering the Dataset #1
by Jake Elwes
Digital Video mp4 / h264
About the Artwork
‘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 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 (2019 - ongoing) 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 concept and tool, and is complicit in reproducing social bias. Zizi combines these themes through a deepfake, 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. A Style-Based Generator Architecture for Generative Adversarial Networks (2019).
Jake Elwes looks for poetry and narrative in the success and failures of these systems, while also investigating and questioning the code and ethics behind them. 'Zizi - Queering the Dataset' is on view at Gazell.io in London in Summer 2021. It was originally commissioned by The University of Edinburgh in 2019 for Experiential AI at Edinburgh Futures Institute and Inspace. His work has also been exhibited in museums and galleries internationally, including the ZKM, Today Art Museum and Victoria and Albert Museum.
Verisart certified: https://verisart.com/works/jake-elwes-225ce181-43c2-4c06-a142-ce1f5f67b584
File size: 37.5 MB
Contract Address: 0xb932a70A57673d89f4acfFBE830E8ed7f75Fb9e0
Token Standard: ERC-721