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Zizi - Queering the Dataset #3

by Jake Elwes

video (MP4)

1024x1024 px

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About the artist

Jake Elwes is a media artist whose explorations into machine learning and artificial intelligence expose their anthropomorphic partialities and spontaneities. The artist finds poetry and narrative in these systems' success and failures, their sophistication, and limitations, while investigating their code and ethics. In notable work, the Zizi Project, Elwes exposes AI bias by queering datasets with drag performers, simultaneously demystifying and subverting AI systems.

Elwes lives and works in London, having studied at The Slade School of Fine Art, UCL (2013-17). The artist's work has been exhibited in museums and galleries internationally, including the ZKM, Karlsruhe, Germany; TANK, Shanghai, China; Today Art Museum, Beijing, China; CyFest, Venice, Italy; Edinburgh Futures Institute, UK; Zabludowicz Collection, London, UK; Frankfurter Kunstverein, Germany; New Contemporaries 2017, London, UK; Ars Electronica 2017, Linz, Austria; Victoria and Albert Museum, London, UK; LABoral Centro de Arte y Creación, Gijón, Spain; Nature Morte, Delhi, India; RMIT Gallery, Melbourne, Australia; Centre for the Future of Intelligence, Cambridge, UK. They have been featured on ZDF aspekte (Germany) and the BBC Arts (UK).

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 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.


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File size: 37 MB

Contract Address: 0xb932a70A57673d89f4acfFBE830E8ed7f75Fb9e0

Token Standard: ERC-721

Blockchain: Ethereum

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Zizi - Queering the Dataset
Zizi - Queering the Dataset