Xeno-Tuning

Dissolving Hegemonic Identities in Algorithmic Multiplicity

Authors

DOI:

https://doi.org/10.7146/aprja.v13i1.151237

Keywords:

Algorithmic Image, Xenofeminism, Queer technologies, Speculative Thinking

Abstract

Xenoimage Dataset is an artistic practice that unleashes the hallucinatory capacities of image-generating AI to question the perpetuation of power dynamics inherent in normative gender dichotomies. Employing techniques called xeno-tuning, it adapts pre-trained models to produce weird representations of corporealities, criticising the homogenous tendencies and biases inherent in image datasets. The purpose is to define the visuality of the xeno as a transformative agent of current hegemonic identities.

Author Biography

Esther Rizo-Casado , ESIC University, Madrid; Complutense University of Madrid

Esther Rizo-Casado serves as a lecturer at ESIC University in Madrid and a researcher at the Complutense University of Madrid (UCM). In addition to her academic roles, she is an accomplished artist and an active member of the XenoVisual Studies collective. With a deep commitment to advancing the integration of women in art and technology, she fosters community engagement through collaborative interdisciplinary projects, supported by the Spanish Ministry of Equality and the UCM Equality Unit (2023-24). Rizo-Casado is the author of a seminal book on life-centered design (2021) and has produced speculative writings that investigate into the intersections of xenofeminism and emerging visual technologies. Her current residency at the contemporary creation center Medialab Matadero Madrid (2023-24) explores innovative perspectives on technology as an enhancer of the boundaries of current hegemonic realities.

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Published

2024-11-19

Issue

Vol. 13 No. 1 (2024): Content/Form

Section

Articles

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Copyright (c) 2024 Esther Rizo-Casado

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