In October 2026, Quilt Ojar travelled to Toronto in a new digital form, becoming part of an experiment exploring what happens when Indigenous art from the Arctic and beyond meets artificial intelligence (AI).
The session Ripples in the Model: Quilt Ojar, Digital Ojar was presented as part of the 50th-anniversary Annual Meeting of the Society for Social Studies of Science (4S), held in Toronto on October 7–10, 2026. Digital Ojar explored the possibilities and limitations of using AI as a co-creator of this online exhibition. Through this experiment, we sought to understand how AI engages with Indigenous knowledges and artistic practices, and what opportunities and challenges emerge from these encounters. We do not present this approach as a model to follow, but rather as an invitation to ask questions and engage in critical reflection. As we worked to digitize the quilt, broader questions about the place and status of marginalized knowledge in digital spaces came to the forefront. Should Indigenous communities’ data be included in AI training datasets to make their knowledge more visible? Should it be included at all? And should—or can—AI-generated art draw on Indigenous artistic traditions?
Our experiment began with an understanding that many Indigenous communities are reluctant to share their knowledge with digital platforms and companies they do not know or trust. In this context, the CARE Principles for Indigenous Data Governance: Collective Benefit, Authority to Control, Responsibility, and Ethics became particularly valuable in guiding our thinking. These concerns extend beyond AI training models to other digital knowledge commons. Even platforms such as Wikipedia, while appreciated for their potential to make knowledge more accessible, are not necessarily fully trusted by Indigenous communities.
Importantly, the artists whose islands come together to form Quilt Ojar agreed to participate in this experiment. Through the Digital Ojar online exhibition, we invite visitors to explore the quilt island by island, encounter its diverse artistic expressions, and reflect alongside us on a pressing question: Where should the limits of AI lie when it encounters Indigenous art, knowledge, and creative practices?
