Techno-positivist narratives of efficiency, productivity, and “democratised” creativity risk obscuring the material, situated realities of creative work; narrowing how HCI can imagine and enable alternative futures. This pictorial presents four futures workshops with seventy creative professionals that surfaced nuanced visions of creative practice with AI in 2030 through structured exploration of three temporal forces: the weight of history, the push of the present, and the pull of the future. We contribute to HCI and DIS by offering a visually-documented framework for futures workshops with creative communities, extending futures-oriented design methods with a structured, dialogic approach that makes ambivalent attitudes toward technology visible rather than reducing them to simple polarities. By translating these discussions into visual vignettes, created in collaboration with an illustrator, we show how pictorials can surface and compare nuances across cohorts, making the work accessible to researchers, educators, and the creative communities that this research is intended to support.

Multimodal AI systems promise more natural, expressive, and creatively rich interactions, yet they also complicate how users convey intent and maintain control. As these systems rapidly enter everyday practice, agency becomes shared between humans and machines, raising urgent questions about: fairness, trustworthiness, responsibility, and accountability; calibrated trust and appropriate reliance; creative authorship and co-creation; meaningful human–AI decision-making. The goal of this workshop is to clarify the concept of distributed agency and explore its implications for human and artificial creativity. Through presentations and hands-on activities, participants will collectively develop human-centered design pathways for multimodal AI interfaces.

While generative AI promises to democratize creativity, we lack empirical understanding of how creative professionals begin using these systems. Through a 10-week longitudinal study following one artist’s self-directed exploration, we ask three case-bounded questions: what interaction patterns emerged? What dynamics characterized sustainable co-creation for this artist? How might we evaluate successful integration in this case? Our findings from this case study reveal: (1) physical environments and material practices drove digital interaction; (2) specific temporal patterns emerged (short prompt bursts, consolidation periods, multi-day resurfacing cycles) and (3) productive discomfort and sustained tension marked successful sessions. These patterns suggest alternative design spaces worth exploring. We contribute: (1) four empirically-grounded design patterns (park-and-resurface, warm-up modes, affect-to-action bridges, comfort controls) that could be prototyped and tested; and (2) speculative provocations that challenge efficiency-first paradigms. This case study demonstrates that some users may benefit from approaches orthogonal to current design paradigms.

As the internet thrives on the circulation of easily copied content, ensuring attribution is properly given has been a perennial challenge. Following the rise of synthetic media and generative AI tools, and corresponding technologies which enable detailed media provenance, the picture has become considerably more complicated. We present a design research project to consider the implications of these developments from the perspective of the public, everyday (non-professional) user and ‘mundane content’ creation. Through the design, exhibition, and study of a collaborative storytelling tool, ORAgen Fables, we introduce technologies which enable detailed attribution and media provenance and explore contemporary attitudes and concerns about attribution. Our findings suggest that attribution should be understood as relational and dynamic with users having the right to ongoing management of their attribution. This opens a design space for understanding how technical systems could be deployed to define and ascribe attribution for past and future interactions.

The proliferation of AI-generated digital content has intensified the user demand for accurate provenance information to ensure content authenticity.Technical advancements now provide tools to make the digital media content supply chain more transparent through the use of provenance data.This paper foregrounds the importance of understanding how the situated nature of user-content engagement influences perceptions and uses of this data.Insights from a workshop with experts in the creative media sector suggest that, as the adoption of provenance data becomes more common, users need richer and more nuanced information.We suggest that analyzing the increasing demand for content authenticity through the lens of multiple "authenticities", each reflecting different user needs and contexts, can help identify and address the needs for, and uses of, provenance data by creators and audiences alike.

We increasingly rely on automated decision-making systems to search for information and make everyday choices. While concerns regarding bias and fairness in machine learning algorithms have high resonance, less addressed is the equally important question of to what extent we are handing our own role of agents over to artificial information-retrieval systems. This paper aims at drawing attention to this issue by considering what agency in decision-making processes amounts to. The main argument that will be proposed is that a system needs to be capable of reasoning in counterfactual terms in order for it to be attributed agency. To reach this step, automated system necessarily need to develop a stable and modular model of their environment.