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G.L. Turtle
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6 records found
1
Queer Zineographies
Materializing Tactics for Resisting AI and Data Systems
Conference paper
(2026)
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Alexandra Teixeira Riggs, Louie Søs Meyer, Molly O'Reilly-Kime, Tommaso Armstrong, Kay Kender, Ekat Osipova, Anh Ton Tran, Jordan Taylor, Grace Leonora Turtle, More Authors
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.
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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.
Sounding Territories
Dis/identificatory codings as relational worlding
This paper presents Sounding Territories, a sonic performance developed with Fundación Organizmo (Colombia) that explores dis/identificatory codings as an orientation for resisting and subverting algorithmic capture. Building on queer and more-than-human perspectives, the project reorients computational logics from stable classification toward relational and generative forms of dis/identification. Through deep listening, embodied performance, and model training, datasets were composed as relational constellations: entanglements of sounds and situations (e.g., fire–conversation–bird–wind) that enact the liveliness of data beyond representational frames. Rather than seeking legibility within existing taxonomies, dis/identification becomes a methodological reorientation— a performance of politics—that disturbs predictive logics to embrace illegibility as a generative principle. The paper contributes to more-than-human design by advancing dis/identificatory coding as a mode of relational worlding.
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This paper presents Sounding Territories, a sonic performance developed with Fundación Organizmo (Colombia) that explores dis/identificatory codings as an orientation for resisting and subverting algorithmic capture. Building on queer and more-than-human perspectives, the project reorients computational logics from stable classification toward relational and generative forms of dis/identification. Through deep listening, embodied performance, and model training, datasets were composed as relational constellations: entanglements of sounds and situations (e.g., fire–conversation–bird–wind) that enact the liveliness of data beyond representational frames. Rather than seeking legibility within existing taxonomies, dis/identification becomes a methodological reorientation— a performance of politics—that disturbs predictive logics to embrace illegibility as a generative principle. The paper contributes to more-than-human design by advancing dis/identificatory coding as a mode of relational worlding.
Queering AI (Mariconiando a la AI)
(Re)orienting Design Practice Towards Co-Predictive Relations
Predictive AI systems increasingly shape social and technological life by abstracting from situated knowledge and experience, encoding human and nonhuman entities into fixed categories, and constraining indeterminate and queer senses of futurity. This dissertation develops a more-than-human design practice of queering AI, reconfiguring predictive systems toward co-predictive, relational ways of knowing and worlding. Drawing on autotheory as a queer methodology grounded in lived experience, the research advances three experimental engagements—Mutant in the Mirror, Undoing Gracia, and Sounding Territories—that recode predictive systems from within. Mutant in the Mirror uses a style-based Generative Adversarial Network (StyleGAN) to generate self-portraits that explore AI as a nonbinary entity, resisting fixed recognition systems. Undoing Gracia inhabits algorithmic borderlands through a multi-agent simulation grounded in autobiographical data, tracing how plural subjectivities emerge through interaction with a digital twin. Sounding Territories foregrounds embodied, more-than-human relations through a sound-generative model that embraces dis/identificatory codings and resists algorithmic capture. Working from a situated queer mestiza ethics, these experiments traverse AI development from data capture through post-training interaction by composing datasets, configuring models and simulations, and enacting co-performances.
Synthesizing what these experiments make perceptible in practice, the dissertation distills three queer practice (re)orientations—trans/mutations (toward indeterminacy), algorithmic borderlands (toward thresholds), and dis/identificatory codings (toward illegibility)—and translates them into tactics for design research with predictive systems. The research yields three contributions: 1) introduces co-predictive relations as a theoretical framework countering logics of separability in predictive systems; (2) advances autotheory as an epistemically generative queer design method; and (3) offers redirective pathways for designers and scholars to cultivate desirable un/predictability and queer futurities. By positioning queering as a mode of intervention within AI research, the dissertation contributes to Human Computer Interaction, Society and Technology Studies, design, and futures studies. Through this, the thesis shifts queer discourse from identity-based legibility toward a politics of possibility, concerned not only with who is rendered recognizable by AI, but with how worlds are made and transformed through the design of co-predictive relations.
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Synthesizing what these experiments make perceptible in practice, the dissertation distills three queer practice (re)orientations—trans/mutations (toward indeterminacy), algorithmic borderlands (toward thresholds), and dis/identificatory codings (toward illegibility)—and translates them into tactics for design research with predictive systems. The research yields three contributions: 1) introduces co-predictive relations as a theoretical framework countering logics of separability in predictive systems; (2) advances autotheory as an epistemically generative queer design method; and (3) offers redirective pathways for designers and scholars to cultivate desirable un/predictability and queer futurities. By positioning queering as a mode of intervention within AI research, the dissertation contributes to Human Computer Interaction, Society and Technology Studies, design, and futures studies. Through this, the thesis shifts queer discourse from identity-based legibility toward a politics of possibility, concerned not only with who is rendered recognizable by AI, but with how worlds are made and transformed through the design of co-predictive relations.
...
Predictive AI systems increasingly shape social and technological life by abstracting from situated knowledge and experience, encoding human and nonhuman entities into fixed categories, and constraining indeterminate and queer senses of futurity. This dissertation develops a more-than-human design practice of queering AI, reconfiguring predictive systems toward co-predictive, relational ways of knowing and worlding. Drawing on autotheory as a queer methodology grounded in lived experience, the research advances three experimental engagements—Mutant in the Mirror, Undoing Gracia, and Sounding Territories—that recode predictive systems from within. Mutant in the Mirror uses a style-based Generative Adversarial Network (StyleGAN) to generate self-portraits that explore AI as a nonbinary entity, resisting fixed recognition systems. Undoing Gracia inhabits algorithmic borderlands through a multi-agent simulation grounded in autobiographical data, tracing how plural subjectivities emerge through interaction with a digital twin. Sounding Territories foregrounds embodied, more-than-human relations through a sound-generative model that embraces dis/identificatory codings and resists algorithmic capture. Working from a situated queer mestiza ethics, these experiments traverse AI development from data capture through post-training interaction by composing datasets, configuring models and simulations, and enacting co-performances.
Synthesizing what these experiments make perceptible in practice, the dissertation distills three queer practice (re)orientations—trans/mutations (toward indeterminacy), algorithmic borderlands (toward thresholds), and dis/identificatory codings (toward illegibility)—and translates them into tactics for design research with predictive systems. The research yields three contributions: 1) introduces co-predictive relations as a theoretical framework countering logics of separability in predictive systems; (2) advances autotheory as an epistemically generative queer design method; and (3) offers redirective pathways for designers and scholars to cultivate desirable un/predictability and queer futurities. By positioning queering as a mode of intervention within AI research, the dissertation contributes to Human Computer Interaction, Society and Technology Studies, design, and futures studies. Through this, the thesis shifts queer discourse from identity-based legibility toward a politics of possibility, concerned not only with who is rendered recognizable by AI, but with how worlds are made and transformed through the design of co-predictive relations.
Synthesizing what these experiments make perceptible in practice, the dissertation distills three queer practice (re)orientations—trans/mutations (toward indeterminacy), algorithmic borderlands (toward thresholds), and dis/identificatory codings (toward illegibility)—and translates them into tactics for design research with predictive systems. The research yields three contributions: 1) introduces co-predictive relations as a theoretical framework countering logics of separability in predictive systems; (2) advances autotheory as an epistemically generative queer design method; and (3) offers redirective pathways for designers and scholars to cultivate desirable un/predictability and queer futurities. By positioning queering as a mode of intervention within AI research, the dissertation contributes to Human Computer Interaction, Society and Technology Studies, design, and futures studies. Through this, the thesis shifts queer discourse from identity-based legibility toward a politics of possibility, concerned not only with who is rendered recognizable by AI, but with how worlds are made and transformed through the design of co-predictive relations.
Undoing Gracia
Queering the self in the algorithmic borderlands
This paper introduces queering as a methodological intervention in human–AI entanglements, aimed at pluralizing the self and resisting normative algorithmic logics. Expanding the role of queerness in HCI beyond its traditional associations with gender and sexuality, we conceptualize queering as a relational, performative, and political disturbance that disrupts the interpellative forces of AI systems. Our exploration unfolds through Undoing Gracia, an autotheoretical experiment seeded with the first author’s autobiographical memories and values, in which Grace interacts with two digital twin agents, Lex and Tortugi, within the speculative world of Gracia. This multi-agent simulation probes the algorithmic borderlands of subjectivity, as the self is iteratively co-performed and transformed through interaction with the agents. Rather than focusing on technical intervention, the experiment explores co-performance, opening new directions for designing human–AI relations grounded in relationality, plurality, and speculative experimentation. The first author designed and performed the autotheoretical experiment, while the co-authors contributed to the theoretical articulation and critical analysis.
...
This paper introduces queering as a methodological intervention in human–AI entanglements, aimed at pluralizing the self and resisting normative algorithmic logics. Expanding the role of queerness in HCI beyond its traditional associations with gender and sexuality, we conceptualize queering as a relational, performative, and political disturbance that disrupts the interpellative forces of AI systems. Our exploration unfolds through Undoing Gracia, an autotheoretical experiment seeded with the first author’s autobiographical memories and values, in which Grace interacts with two digital twin agents, Lex and Tortugi, within the speculative world of Gracia. This multi-agent simulation probes the algorithmic borderlands of subjectivity, as the self is iteratively co-performed and transformed through interaction with the agents. Rather than focusing on technical intervention, the experiment explores co-performance, opening new directions for designing human–AI relations grounded in relationality, plurality, and speculative experimentation. The first author designed and performed the autotheoretical experiment, while the co-authors contributed to the theoretical articulation and critical analysis.
Conference paper
(2022)
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Grace L. Turtle, Carlos Guerrero Millan, Seda Özçetin, Mugdha Patil, Roy Bendor
The Sensing in the Wild Lab is a speculative experiment in designing a de- centralised urban sensing system from a more-than-human perspective. It is part of DCODE, an H2020-ITN project that explores the future of designing with AI. During the Lab participants assume different identities – roleplaying as children but also as moss, as municipal authorities, as CCTV cameras, as pigeons, and as undocumented immigrants trying to evade the authorities – and are asked to feed into the sensing system data that reflects their particular perspectives and interests. The data partici- pants share, in the form of an image and text uploaded to a dedicated WhatsApp channel, helps to reveal both frictions and alignments among actors. In this, the Lab offers municipalities an opportunity to shift their thinking about the future smart city from a “system of systems” that is optimised for a few city dwellers to a much more distributed, inclusive meshwork in which data is contributed, circulated, and negoti- ated by humans and nonhumans alike.
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The Sensing in the Wild Lab is a speculative experiment in designing a de- centralised urban sensing system from a more-than-human perspective. It is part of DCODE, an H2020-ITN project that explores the future of designing with AI. During the Lab participants assume different identities – roleplaying as children but also as moss, as municipal authorities, as CCTV cameras, as pigeons, and as undocumented immigrants trying to evade the authorities – and are asked to feed into the sensing system data that reflects their particular perspectives and interests. The data partici- pants share, in the form of an image and text uploaded to a dedicated WhatsApp channel, helps to reveal both frictions and alignments among actors. In this, the Lab offers municipalities an opportunity to shift their thinking about the future smart city from a “system of systems” that is optimised for a few city dwellers to a much more distributed, inclusive meshwork in which data is contributed, circulated, and negoti- ated by humans and nonhumans alike.