Human aesthetics under the representational power of Artificial Intelligence

• Autores: Piera Riccio
• Directores de la Tesis: Miguel Ángel Lozano Ortega (tut. tes.) , Nuria M. Oliver Ramírez (dir. tes.)
• Lectura: En la Universitat d'Alacant / Universidad de Alicante ( España ) en 2025
• Idioma: español
• Tribunal Calificador de la Tesis: Nanne van Noord (presid.) , Francisco Gómez Donoso (secret.) , Cha Meeyoung (voc.)
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• Resumen

  • This thesis investigates how Artificial Intelligence (AI)-based technologies mediate human representation in contemporary visual culture. The work is grounded on Don Ihde’s philosophical framework that analyzes distinct modalities (embodiment, hermeneutic, and alterity) according to which humans relate to technologies. Through a combination of technical contributions, along with critical reflections on the socio-ethical and artistic dimensions of these systems, the thesis offers an interdisciplinary exploration of AI’s role in visual culture. First, we examine augmented reality (AR) beauty filters as a form of embodiment relation, where the technology becomes transparent and modifies how individuals perceive and present their own faces. By introducing novel datasets (FairBeauty and B-LFW) and the OpenFilter tool, we demonstrate how these filters propagate Eurocentric beauty standards, subtly reshaping identity in ways that reinforce historical and racialized aesthetics. Then, we address the algorithmic censorship of artistic nudity as a hermeneutic relation, focusing on how moderation systems interpret and assess the obscenity of the human body. Through a mixed-methods approach that combines qualitative and quantitative contributions, the chapter reveals the limitations of current moderation technologies and advocates for greater transparency, cultural sensitivity, and accountability in content moderation governance. Finally, we explore text-to-image (T2I) generative systems through the lens of alterity relation, highlighting how users interact with technologies that produce outputs perceived as novel and autonomous. By auditing leading T2I platforms and introducing ImageSet2Text, a new method for summarizing image sets via vision-language models, we uncover stylistic patterns and cultural biases embedded in AI-generated depictions of humans.