As AI and data systems often falter when encountering queer identities and knowledge, reinforcing existing oppressions, queer people have resisted such systems and their normalizing tendencies. This pictorial explores tactics of queering AI through a collaborative zine-making project (i.e. zineography) that challenges generative AI and data systems. We share how we workshopped and materialized queering tactics in zine spreads; analyzed these spreads according to materials, content, and tone; and visualized our analysis as thematic collages. We contribute: (1) tangible characteristics of queering AI and data systems (i.e. materials, tones, and aesthetics); and (2) design opportunities for using zineographies as a radical method for building and collectively sharing knowledge about a marginalized community, including recommendations for enacting queer zineographies. By materializing queering tactics through zine-making, we invite embodied, action-oriented critiques that question dominant techno-solutionist movements and trace queer possibilities outside of their normalizing narratives.

Machine Learning (ML) models are increasingly applied to wearable self-tracking technologies to offer daily classifications and recommendations for well-being. This shift introduces design challenges, particularly regarding the opacity of training processes and model outputs. We contribute to this space with a conceptual framing of the algorithmic gaze on body and well-being, which we use to critically investigate long-term engagement with a wearable self-tracking technology. Through an autoethnographic study with the Oura Ring, we identified three themes, highlighting tensions between wearer and the ML models, namely: Conflicting narratives of daily activities, fine-tuning of the human, and blurry boundaries of multiple bodies using such devices simultaneously. Departing from the themes, we used fabulation as a method to craft narratives that probe the tensions from the algorithmic gaze, from which we offer alternative design openings for ML in wearable self-tracking devices.

This Research through Design paper explores how object detection may be applied to a large digital art museum collection to facilitate new ways of encountering and experiencing art. We present the design and evaluation of an interactive application called SMKExplore, which allows users to explore a museum’s digital collection of paintings by browsing through objects detected in the images, as a novel form of open-ended exploration. We provide three contributions. First, we show how an object detection pipeline can be integrated into a design process for visual exploration. Second, we present the design and development of an app that enables exploration of an art museum’s collection. Third, we offer reflections on future possibilities for museums and HCI researchers to incorporate object detection techniques into the digitalization of museums.