In this third installment of our GenAI workshop series at DIS, we focus on ‘stopsigns’—the blockages that impede progress in design research with GenAI. These stopsigns manifest as both semantic barriers (political, social, or mental frameworks) and pragmatic hurdles (technical limitations or implementation challenges) that persist despite the rapid advancements since the GenAI boom. Such stopsigns present a productive tension—they often contain partial truths worthy of consideration while simultaneously being shortsighted in ways that prevent progression. From blanket rejection to uncritical acceptance, these barriers affect how meaningfully we engage with GenAI’s potential. Our workshop welcomes both returning and first-time participants to share their experiences with these persistent challenges and work together to develop practical solutions. Through analysis of real cases and hands-on activities, we’ll build strategies for moving beyond these obstacles while acknowledging their legitimate concerns. Our goal is to foster more thoughtful integration of GenAI in design research and practice.

As Artificial Intelligence continues to permeate everyday life, concerns over its societal consequences are becoming increasingly pressing. Anticipatory practices have emerged as central to responsible AI development, offering ways to envision and mitigate potential harms. While policymakers engage with anticipation through forecasting and risk assessment, speculative design offers an alternative, more experiential approach to also fosters public engagement and critical reflection. However, most speculative explorations focus on future possibilities, often neglecting the continuum between these and past phenomena. In this pictorial, we argue for integrating historical perspectives into speculative design to enrich anticipatory work on AI. Through a week-long international summer school, we engaged with the legacy of phrenology and the work of Cesare Lombroso. Using this as a springboard for speculation, we illustrate that incorporating historical trajectories into speculative design can deepen understanding of current dilemmas around AI, but dedicated methodological resources are still needed to achieve this value.

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.

Data is central to AI performance, yet its curation remains an invisible and labour-intensive process, often leading to biases and reliability issues. While HCI has explored methods to improve data work, a growing body of research embraces data imperfections in an artistic vein to provoke reflection on human-AI relations. This workshop examines "Data Craft"- practices that creatively manipulate data to challenge conventional AI narratives and lower barriers to public engagement. By framing data craft as a boundary practice, we explore its potential to foster dialogue on AI capabilities, limitations, and societal impact. The workshop will investigate how data craft can be systematically integrated into participatory AI efforts, moving beyond artistic spaces to inform public debate. Through hands-on exploration, we aim to uncover strategies for leveraging data craft to engage diverse communities in shaping AI discourse.