The Zizi Show
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“If AI holds a mirror up to society, then Zizi applies the makeup.”
Drew Hemment
Drag Queens, Drag Kings, Drag Things and... Artificial Intelligence.
The Zizi Show (2020) is a deepfake drag cabaret, a virtual online stage hosting a groundbreaking new show with a twist. It features acts that have been constructed using deepfake technology, developed in collaboration with the London drag community. *The Zizi Show* dissects one of the dominant myths about AI, the notion that “an AI” is a thing we might mistake for a person. It is also about how we can create our own datasets and take back control, and as a queer community whether we want to be recognized by these systems, and how can we queer these systems.
The bodies in the show have been generated by neural networks trained on a community of drag artists who were filmed to create training datasets at a London cabaret venue closed during COVID-19. The project opened up safe spaces for creative expression, in person and online.
The Zizi Show constructs and then deconstructs a virtual cabaret that pushes the limits of what can be imagined on a digital stage. During each act audiences are invited to interact with the website and play with which deepfake bodies perform which songs. The deepfakes were created collaboratively in celebration, resisting the exploitative and oppressive nature of deepfakes. At times this breaks down when the AI tries to conceive impossible positions or combines multiple different queer identities; it can even reveal the skeleton tracking the deepfake is built on.
The Zizi Project (2019 – ongoing) is a collection of works by Jake Elwes exploring the intersection of AI and drag performance. It is a partnership with the Experiential AI research group at Edinburgh Futures Institute. Drag challenges gender and explores otherness, while AI 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 AI.
“If AI holds a mirror up to society, then Zizi applies the makeup.”
Drew Hemment
Drag Queens, Drag Kings, Drag Things and... Artificial Intelligence.
The Zizi Show (2020) is a deepfake drag cabaret, a virtual online stage hosting a groundbreaking new show with a twist. It features acts that have been constructed using deepfake technology, developed in collaboration with the London drag community. *The Zizi Show* dissects one of the dominant myths about AI, the notion that “an AI” is a thing we might mistake for a person. It is also about how we can create our own datasets and take back control, and as a queer community whether we want to be recognized by these systems, and how can we queer these systems.
The bodies in the show have been generated by neural networks trained on a community of drag artists who were filmed to create training datasets at a London cabaret venue closed during COVID-19. The project opened up safe spaces for creative expression, in person and online.
The Zizi Show constructs and then deconstructs a virtual cabaret that pushes the limits of what can be imagined on a digital stage. During each act audiences are invited to interact with the website and play with which deepfake bodies perform which songs. The deepfakes were created collaboratively in celebration, resisting the exploitative and oppressive nature of deepfakes. At times this breaks down when the AI tries to conceive impossible positions or combines multiple different queer identities; it can even reveal the skeleton tracking the deepfake is built on.
The Zizi Project (2019 – ongoing) is a collection of works by Jake Elwes exploring the intersection of AI and drag performance. It is a partnership with the Experiential AI research group at Edinburgh Futures Institute. Drag challenges gender and explores otherness, while AI 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 AI.
Artist, coder & producer: Jake Elwes
Director of Drag: Me the Drag Queen
Web & development: Alexander Hill
Camera & lighting: Toby Elwes
Filming location (LGBTQ+ Cabaret Venue):The Apple Tree
Cast of drag artists: Bolly-Illusion, Cara Melle, Chiyo, Dakota Schiffer, Lilly SnatchDragon, Luke Slyka, Mahatma Khandi, Mark Anthony, Me, Oedipussi Rex, Ruby Wednesday, Sister Sister, Tete Bang
The Zizi Show is part of The New Real by Edinburgh Futures Institute at Edinburgh International Festival
Jake Elwes (GB) is an artist living and working in London. Searching for poetry and narrative in the success and failures of AI systems, Elwes investigates the aesthetics and ethics inherent to them. Elwes’s practice makes use of machine learning’s sophistication while finding illuminating qualities in its limitations. Across projects that encompass moving image installation, sound and performance, Elwes seeks to queer datasets, demystifying and subverting predominantly cisgender and straight AI systems. While it may seem like the AI is a creative collaborator, Elwes is careful to point out that the AI has neither intentionality nor agency; it is a neutral agent existing within a human framework.
Jake Elwes (GB) is an artist living and working in London. Searching for poetry and narrative in the success and failures of AI systems, Elwes investigates the aesthetics and ethics inherent to them. Elwes’s practice makes use of machine learning’s sophistication while finding illuminating qualities in its limitations. Across projects that encompass moving image installation, sound and performance, Elwes seeks to queer datasets, demystifying and subverting predominantly cisgender and straight AI systems. While it may seem like the AI is a creative collaborator, Elwes is careful to point out that the AI has neither intentionality nor agency; it is a neutral agent existing within a human framework.