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Paper short abstract

This paper argues that archival theory offers critical AI studies a generative theoretical resource. Drawing on our experience editing a SI on AI and Archives, we argue that archival theories provide meaningful frameworks for exploring how AI reconfigures the politics of knowledge infrastructures.

Paper long abstract

Recent debates in critical AI research have foregrounded questions of data governance, documentation, classification, and accountability that archival scholars will immediately recognize. Yet despite this convergence, archival theory remains underutilized as a conceptual resource in critical AI studies. This paper argues that archival theory offers not merely resonant vocabulary but a distinct analytical tradition shaped by the institutional, ethical, and political dimensions of many of the problems now foregrounding in AI governance debates. Drawing on our experience editing a special issue on AI and Archives, we trace what we describe as an emerging archival turn in critical AI research. Archival concepts including provenance, appraisal, custody, and unlearning have re-emerged as central sites of ethical and political concern in AI, while critiques of benchmarks and taxonomies echo archival scholarship on how classificatory infrastructures sediment racialized, gendered, and colonial power. We argue that this convergence is symptomatic of shared concerns about who controls the cultural record in the age of AI, on what authority, and to what ends. Rather than treating archives merely as data sources for AI systems, we show how archival theory, in particular its critical and reparative traditions, provides frameworks for interrogating how AI reconfigures memory, authority, and the politics of knowledge production. In doing so, we make a case for archival theory as a generative resource for answering the question this panel poses: what does it mean to be critical about AI?