Zizi - Queering the Dataset

What can drag teach AI?

Jake Elwes

This exhibit 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, causing the weights inside the neural network to shift away from the normative identities it was originally trained on and into a space of queerness. The exhibit is a celebration of difference and ambiguity, which invites us to reflect on bias in our data-driven society.

ABOUT THE ARTIST

Jake Elwes is a media artist living and working in London. He studied at The Slade School of Fine Art, UCL (2013-17). Recent works explore his research into machine learning and artificial intelligence. His practice looks for poetry and narrative in the success and failures of these systems, while also investigating and questioning the code and ethics behind them. His current works in the Zizi Project explore AI bias by queering datasets with drag performers. They simultaneously demystify and subvert AI systems.

Jake's work has been exhibited in museums and galleries internationally, including the ZKM, Karlsruhe, Germany; TANK Museum, Shanghai; Today Art Museum, Beijing; CyFest, Venice; Edinburgh Futures Institute, UK; Zabludowicz Collection, London; Frankfurter Kunstverein, Germany; New Contemporaries 2017, UK; Ars Electronica 2017, Austria; Victoria and Albert Museum, London; LABoral Centro, Spain; Nature Morte, Delhi, India; RMIT Gallery, Australia; Centre for the Future of Intelligence, UK and he has been featured on ZDF & BBC.