Deepfake Abuse and Gendered Digital Creative Violence: Feminist AI Interventions from Mexico
Generative AI is transforming image-based sexual abuse and exposing fundamental limitations in existing trust and safety frameworks. While research has focused on the technical details of detection and platform governance, less attention has been paid to how generative AI changes the nature of harm itself. Drawing on an intersectional feminist study of interviews with 12 survivors, legal advocates, activists, technologists, and researchers in Mexico, this article introduces gendered digital creative violence to explain how generative AI weaponizes creative production to generate emotional, reputational, psychological, and structural harm. We show that deepfake abuse extends beyond synthetic content to encompass victim-blaming, institutional failure, evidentiary instability, and affective exhaustion, leaving survivors to shoulder the burden of digital safety. At the same time, feminist organizations have developed AI-enabled counter-infrastructures, most notably the survivor-support chatbot OlimpIA, demonstrating how AI can be redesigned around care, accompaniment, and collective protection. We argue that creative violence offers a transferable framework for understanding emerging forms of generative harm beyond deepfakes by shifting attention from reactive content moderation to the politics of creation itself. This perspective advances trust and safety scholarship by proposing feminist approaches to AI governance grounded in structural prevention, situated ethics, cross-sector collaboration, and survivor-centered design.