How would you feel if you saw an image of yourself doing something you didn’t do? How would you feel knowing said image was created without your consent? For many people, especially women, these questions are not just hypothetical. Technological advances in Artificial Intelligence (AI), specifically Generative AI, have made it extremely easy and cheap to generate and distribute (non-consensual) synthetic images and videos that depict real people’s voices, faces, or bodies (i.e., deepfakes). Non-consensual synthetic imagery often depicts intimate and sexually explicit scenarios and is considered a form of sexual abuse. We demonstrate Photo BOO-th, an interactive installation designed to turn the creation of non-consensual intimate imagery into a visceral, creepy experience. Through this experience, we invite attendees to grapple with the questions above, discuss the societal harms associated with creating and distributing non-consensual synthetic imagery, and critique how consent is understood and enacted between people and technology.

Personal data flows across digital technologies integrated into people’s lives and relationships. Increasingly, these technologies include Generative AI. (How) should personal data flow into and out of GenAI models? We investigate how people experience personal data collection in GenAI ecosystems and unpack the enablers and barriers to governing their data. We focus on personal data collection by Meta, specifically Instagram, in line with their recent policy update on processing user data to train GenAI models. We conducted semi-structured interviews with 20 Latin American Instagram users, based in Europe and Latin America. We discussed the acceptability of their data flowing in and out of GenAI models through different scenarios. Our results interrogate power dynamics in data collection, the (inter)personal nature of data, and the multiple unknowns concerning data and their algorithmic derivatives. We pose provocations around feelings of powerlessness, reframing (inter)personal data, and encountering unknown data and algorithms through design.