Treats the architecture of latent diffusion image models as an ideological text; tracing component by component what training & generation pipelines displace to convert social communication into vectors.
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Argues that acknowledging real improvement in models and automation systems sharpens rather than concedes critical work, since the durable questions are about what usefulness means, for whom, and what gets removed from public deliberation.
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The AI research community plays a vital role in shaping the scientific, engineering, and societal goals of AI research. In this position paper, we argue that focusing on the highly contested topic of `artificial general intelligence' (`AGI') undermines our ability to choose effective goals.
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A framework for the creative misuse of generative AI systems, developed with the Algorithmic Resistance Research Group (ARRG!) which presented an exhibition at the White House-backed AI Village at the hacker convention DEFCON 31 in 2023.
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On AI as a technology for manufacturing excuses: how "efficiency" claims dislodge civil servants who embody an institution, and why failures would nonetheless consolidate rather than discredit the power of the people who design AI systems.
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A taxonomy of the myths that structure public understanding of generative AI: Emergence, Scale, Creativity, Learning, Prompting / Control, and Productivity.
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Using my own snapshots as a GAN training set, I ask how machine reproduction alters personal memory, drawing photography and cybernetics together to theorize the AI image on its own terms.
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A first-person account of GAN photography as artistic research, arguing that assembling a training set teaches the photographer to see like a dataset: to seek repeatable patterns and discard the outliers that photography traditionally prizes.
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A media studies approach to reading AI generated images as infographics, or visualizations of the underlying dataset.
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