Cohort: SOMET 41

Topic of the thesis: AI and the Visual Politics of Gender: A Multimethod Approach to Algorithmic Oppression

Abstract: In the last few years, visually-led AI technologies have grown in popularity, enabling users to engage in various co-creative algorithmic practices. They represent a fundamental turning point in digital culture to the same extent as the implementation of face filters on social media platforms in 2015, both embodying a visual shock that distorts, reshapes, and questions people's representations. As products of scientific knowledge and computational processes, AI technologies are perceived as neutral and objective, holding significant cultural and epistemic legitimacy. The objectivity that arises from these technological tools leads to crucial questions on what can be represented and promoted as generic and normative. This research wants to shed a light on the notion of algorithmic oppression within visual culture, focusing on the relationship between face filters and AI tools and the representation of gender identities. To accomplish that, this research adopts a multimethod approach that mobilizes qualitative and quantitative methods to research the production, circulation, and reception of AI-generated visuals on Instagram and GAN face filters on TikTok. My hypothesis is that both face filters and text-to-image AI tools should be framed as technologies of gender as they work to produce, promote, and mobilize narrow and specific gender representations over time.

Research interests: Visual communication; Gender and technology studies; AI and society; Social semiotics;
Graduated from: University of Bologna
Degrees obtained: Visual arts, music, performing arts and fashion studies (BA); Semiotics (MA)
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