Jake Elwes
Queering the Dataset - Zizi in space, 2021
Edition of 10
C-type print
Unsigned
Unframed: 33cm (W) x 33cm (H)
Arrives with a certificate of authenticity.
Delivery charges are calculated after purchase and invoiced separately.
C-type print
Unsigned
Unframed: 33cm (W) x 33cm (H)
Arrives with a certificate of authenticity.
Delivery charges are calculated after purchase and invoiced separately.
Zizi – Queering the Dataset by Jake Elwes aims to tackle the lack of representation and diversity in the training datasets often used by facial recognition systems. The prints are...
Zizi – Queering the Dataset by Jake Elwes aims to tackle the lack of representation and diversity in the training datasets often used by facial recognition systems. The prints are drawn from a process developed by disrupting these systems* and retraining 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 works are a celebration of difference and ambiguity, inviting us to reflect on bias in our data-driven society.
*A Style-Based Generator Architecture for Generative Adversarial Networks (2019)
Zizi – Queering the Dataset lets us peek inside the machine learning system and visualise what the neural network has (and hasn’t) learnt. The works are a celebration of difference and ambiguity, inviting us to reflect on bias in our data-driven society.
*A Style-Based Generator Architecture for Generative Adversarial Networks (2019)
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