Laura Wagner

Hello there! Welcome to my personal page.

I am Laura Wagner, a PhD student at the University of Zürich. My research is embedded within the project“The Canon of Latent Spaces: How Large AI Models Encode Art and Culture” led by Eva Cetinic and generously supported by the Swiss National Science Foundation. Currently I investigate the vernacular of open-source text-to-image and video generative AI models with a focus on how they are adopted, personalized and shared in the wild. Particularly of interest is to me,how norms and practices from online communities become encoded into the models themselves, through tagging, retraining, and sharing, and then perpetuate through visual output at scale.

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news

Sep 21, 2026	Masterclass at UPDATE Festival, Liepāja Held the online masterclass 'Latent Vandalism: The Joy of Productive Damage to Text-to-Image Synthesis Pipelines' (in two parts) at UPDATE Festival, Liepāja.
Jul 28, 2026	Paper Published in Big Data & Society 'Perpetuating Misogyny with Generative AI: How Model Personalization Normalizes Gendered Harm', co-authored with Eva Cetinić, is now out in Big Data & Society.
Jul 27, 2026	Artwork Accepted into CVPR 2026 Art Program 'Drawing ImageNet' has been accepted into the CVPR 2026 Art Program, Aesthetics track.
Mar 02, 2026	Workshop – Digital Humanities: Computational Analysis of Cultural Heritage Images, Uppsala University Practical workshop on computational approaches to digital humanities, using Europeana's API, IIIF, and CLIP embeddings to analyse European cultural heritage image collections.
Feb 12, 2026	Invited Talk – AISthesis Network, Aarhus University Invited talk on 'Cultural Inscriptions and Taxonomies in Text-to-Image Models' at the AISthesis network for research on the aesthetics of AI imagery.
Jan 16, 2026	Workshop at KISD – Köln International School of Design Held 'Latent Vandalism: The Joy of Productive Damage to Text-to-Image Synthesis Pipelines' at KISD. Workshop materials available on GitHub.

selected publications

Perpetuating Misogyny with Generative AI: How Model Personalization Normalizes Gendered Harm

Laura Wagner and Eva Cetinić

Big Data & Society, 2026

Abs DOI arXiv

Open-source text-to-image (TTI) pipelines have become dominant in the landscape of AI-generated visual content, driven by technological advances that enable users to personalize models through adapters tailored to specific tasks. While personalization methods such as LoRA (low-rank adaptation) offer unprecedented creative opportunities, they also facilitate harmful practices, including the generation of non-consensual deepfakes and the amplification of misogynistic or hypersexualized content. This study presents an exploratory sociotechnical analysis of CivitAI, the most active platform for sharing and developing open-source TTI models. Drawing on a dataset of more than 40 million user-generated images and over 230,000 models, we find a disproportionate rise in not-safe-for-work (NSFW) content and a significant number of models intended to mimic real individuals. We also observe a strong influence of internet subcultures on the tools and practices shaping model personalizations and resulting visual media. In response to these findings, we contextualize the emergence of exploitative visual media through feminist and constructivist perspectives on technology, emphasizing how design choices and community dynamics shape platform outcomes. Building on this analysis, we propose interventions aimed at mitigating downstream harm, including improved content moderation, rethinking tool design, and establishing clearer platform policies to promote accountability and consent.