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.

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.