GenAI as Data in Anti‐Racist Linguistic Research: Risks and Possibilities in AI as a Raciolinguistic Infrastructure

ABSTRACT The rapid integration of generative artificial intelligence (GenAI) into applied linguistics has prompted growing interest in using GenAI outputs as research data. Although critical scholarship has examined issues such as bias, privacy, and invisible labor, little attention has been paid to whether GenAI‐generated data are compatible with the epistemological commitments of anti‐racist and decolonial research. This conceptual paper addresses the question: Can anti‐racist linguistic research use GenAI as data? I argue that GenAI should be understood as a raciolinguistic infrastructure embedded in histories of colonialism, racial capitalism, and data colonialism rather than as a neutral producer of language. Drawing on anti‐racist, decolonial, and Southern approaches to applied linguistics, I identify methodological risks associated with treating GenAI outputs as primary data: re‐centering Whiteness and Global North norms, erasing communities by treating models as stand‐ins for speakers, decontextualizing racism and ableism, and reinforcing ethically opaque and extractive infrastructures. At the same time, I outline conditions under which AI can be studied critically as evidence of institutionalized language ideologies and platform governance. The paper concludes by proposing a framework for linguistically just AI research that positions (Gen)AI as an object of critique while maintaining community‐grounded knowledge as the epistemic center of interpretation.