What does this research mean for the field?

A participatory art-science pipeline that grounds AI in everyday experiences and utilizes open licensing can effectively co-produce demystified, alternative visual representations of AI that generate sustained public value. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The project aims to shift perceptions of AI from speculative fiction to real-world applications through participatory art-science efforts.

June 6, 2026Open Access

The Bigger Picture: A Participatory Pipeline for Re-imagining AI Imagery through Art-Science Collaboration

Key Points

The project aims to shift perceptions of AI from speculative fiction to real-world applications through participatory art-science efforts.
Conducted participatory educational workshops engaging 79 participants to explore AI through lived experiences.
Facilitated discussions that generated co-produced themes reflecting participants' insights on AI.
Curated public exhibitions displaying selected artworks and integrating resources into the Better Images of AI library.
Workshops engaged 79 participants, yielding 20 exhibited works viewed by 1,913 visitors.
Eleven images contributed to the Better Images of AI library, reused in over 90 outlets including policy papers.
Demonstrated how participatory projects can create durable public value through shared resources.

Abstract

Current socio-technical imaginaries of Artificial Intelligence (AI) are rooted in speculative fiction - e.g. humanoid robots and glowing brains. These depict AI as an abstract, futuristic phenomenon rather than a ubiquitous element of contemporary life. When the public imagines AI as sentient robots or superintelligence, they fail to recognise everyday AI applications like the recommendation algorithms curating their social media feeds (Leufer et al.; Cave and Dihal; Dihal and Duarte; Singler; Sartori and Bocca). The Bigger Picture 1 is a participatory art-science initiative in Ireland that disrupts these narratives by shifting focus from "sci-fi" abstraction towards representations of AI grounded in everyday applications. Through participatory educational workshops (Hu), the project demystifies AI through collaborative inquiry and creative practice. This paper presents a case study of The Bigger Picture through the lens of knowledge co-production (Jasanoff), examining how participants' experiential knowledge of AI shaped thematic frameworks and visual outputs, generating alternative ways of understanding emerging technologies that challenge established narratives. The participatory pipeline comprises: Participatory Educational Workshops function as co-production environments where participants explore AI through interactive activities focused on lived experiences. By introducing Explainable AI concepts (Ali et al.) alongside ethical issues such as algorithmic bias (Romele; Dubber et al.), workshops draw on participants' everyday AI experiences - from social media algorithms to voice assistants - as situated knowledge (Haraway) that challenges dominant visual stereotypes (Dihal and Duarte). Thematic Translation: Workshop insights are synthesised into co-produced themes (e.g. AI is All Around Us, AI is Human, AI is Complex) emerging from participant deliberation rather than expert pre-determination. These inform a curated call for artistic submissions reflecting everyday AI rather than sci-fi tropes. Exhibition and Digital Resources: The pipeline culminates in public exhibitions. Selected images are integrated into the Better Images of AI library 2 under a Creative Commons CC4.0 license. The pipeline generated measurable impact: workshops engaged 79 participants (24 artists/technologists during Science Week 3; 55 youth participants via Cruinniú na nÓg 4), yielding 20 exhibited works viewed by 1,913 visitors. Eleven images entered the Better Images of AI library and have since been reused in 90+ outlets including the Alan Turing Institute and international policy papers, demonstrating how local engagement can lead to open public resources. The Bigger Picture demonstrates key insights about participatory infrastructure for emerging technologies. First, grounding technologies in everyday experience enabled participants to contribute situated knowledge that experts alone could not generate. Second, translating public insights into creative constraints shaped artistic outputs through collective inquiry. Third, CC licensing transformed ephemeral engagement into durable infrastructure: 90+ reuses suggest participatory projects can generate sustained public value when outputs are contributed to commons. This demonstrates co-produced research conducted 'in conversation with' publics (Fitzpatrick) can inform engaged scholarship on emerging technologies. 1 The Bigger Picture: https://thebiggerpictureai.com/ 2 Better Images of AI Image Library: https://betterimagesofai.org/images 3 Science Week takes place nationwide annually in November Ireland: https://www.scienceweek.ie/ 4 Cruinniú na nÓg is a day of free creativity for young people that takes place nationwide annually in Ireland in June: https://cruinniu.gov.ie/ Works Cited Ali, Sajid, et al. ‘Explainable Artificial Intelligence (XAI): What We Know and What Is Left to Attain Trustworthy Artificial Intelligence’. Information Fusion, vol. 99, Nov. 2023, p. 101805. ScienceDirect, https://doi.org/10.1016/j.inffus.2023.101805. Cave, Stephen, and Kanta Dihal. ‘Hopes and Fears for Intelligent Machines in Fiction and Reality’. Nature Machine Intelligence, vol. 1, no. 2, Feb. 2019, pp. 74–78. www.nature.com, https://doi.org/10.1038/s42256-019-0020-9. Dihal, Kanta, and Tania Duarte. ‘Better Images of AI: A Guide for Users and Creators’. Better Images of AI Blog, 2023, https://blog.betterimagesofai.org/better-images-of-ai-guide/. Dubber, Markus Dirk, et al. The Oxford Handbook of Ethics of AI. Oxford University Press, 2020. Fitzpatrick, Kathleen. Generous Thinking: A Radical Approach to Saving the University. Johns Hopkins University Press, 2021. Google Scholar, https://books.google.com/books?hl=en&lr=&id=2UbzDwAAQBAJ&oi=fnd&pg=PR9&dq=kathleen+fitzpatrick+university&ots=qFDgqYCtfF&sig=qINrUknPu-BzGrnRS5iXMxL1wA0. Haraway, Donna. ‘Situated Knowledges: The Science Question in Feminism and the Privilege of Partial Perspective’. Feminist Studies, vol. 14, no. 3, 1988, pp. 455–72. Hu, Aihua. ‘The Workshop as a Research Methodology: Lessons Learned from Workshops Utilized as a Participatory Research Method’. Metodetilnærminger Og Prosessuelle Design i Barnehageforskning, Universitetsforlaget, 2024, pp. 120–33. Books. scup.com (Atypon), https://doi.org/10.18261/9788215064697-24-07. Jasanoff, Sheila. ‘The Idiom of Co-Production’. States of Knowledge, Routledge, 2004, pp. 1–12. Google Scholar, https://api.taylorfrancis.com/content/chapters/edit/download?identifierName=doi&identifierValue=10.4324/9780203413845-1&type=chapterpdf. Leufer, Daniel, et al. Myth: AI = Shiny Humanoid Robots. https://www.aimyths.org/ai-equals-shiny-humanoid-robots. Accessed 7 Apr. 2025. Romele, Alberto. ‘Images of Artificial Intelligence: A Blind Spot in AI Ethics’. Philosophy & Technology, vol. 35, no. 1, Jan. 2022, p. 4. Springer Link, https://doi.org/10.1007/s13347-022-00498-3. Sartori, Laura, and Giulia Bocca. ‘Minding the Gap(s): Public Perceptions of AI and Socio-Technical Imaginaries’. AI & SOCIETY, vol. 38, no. 2, Apr. 2023, pp. 443–58. Springer Link, https://doi.org/10.1007/s00146-022-01422-1. Singler, Beth. ‘The AI Creation Meme: A Case Study of the New Visibility of Religion in Artificial Intelligence Discourse’. Religions, vol. 11, no. 5, no. 5, May 2020, p. 253. www.mdpi.com, https://doi.org/10.3390/rel11050253. Submission number: 227

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