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曹雪楠教授

曹雪楠教授

助理教授

Ph.D. (The Program of Literature) Duke University
M.Phil. (Humanities and Creative Writing) Hong Kong Baptist University
B.A. (Humanities-Communication Arts) Hong Kong Baptist University

關於 曹雪楠教授

Xuenan Cao is Assistant Professor of Cultural Studies. She holds a GRF grant and several other grants on AI. Her research on machine learning, digital media, China, and writings about literature has been appeared in boundary 2 (2025), Media, Culture & Society (2025), Information, Communciation & Society (2025), PMLA (2024), Big Data & Society (2023), Theory, Culture & Society (2021), Extrapolation (2019), Journal of Language, Literature, and Culture (2016), and Days of Future Pasts: Memorializing the Archive (2025). She also publishes in the field of computer science and computational humanities. Her digital project AI in China: Scketchy Prehistories can be viewed here.

 

Her first book Media Improvisation in China: Thriving on Deficits (contracted at Stanford University Press) offers an angle—of socialist technology developed in conditions of deficiency—which helps explain the unexpected success of DeepSeek. 

 

Her second book AI Extrapolations is in progress. It shows how machine learning models goes beyond their training to generate outputs of long-term cultural consequences. 

 

She teaches the following courses at CUHK:

MA_AI Culture and Society

MA_Digital Culture and Society

MA_The Politics of Cultural Identities

BA_The Cultural Studies of Space

BA_Art, Propaganda, and Social Action

BA_Youth Culture

Before coming to CUHK, she also taught Chinese media and literature at New York University (Shanghai) and Yale University.

 

Reach out to Xuenan here.

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  • 研究興趣
    • Critical AI
    • Media Aesthetics
    • AI Accountability (Extrapolation)
    • Women Studies
    • Working-Class
    • World Literature
  • 近期研究計劃
    1. “What Is ‘New?’ Evaluating GenAI Across Disciplines”
      -2024-2025, a workgroup funded by Research Institute for the Humanities

      From the project:
      How does a AI model claim novelty in its output? This is a main obstacle for developers of AI and a key question that humanities knows best how to answer. This workgroup evaluate the diversity and novelty measurement of Generative AI.

    2. Research on AI explanability and accountability, AI Extrapolation
      - 2023-4, funded by CUHK Faculty of Arts Direct Grant
      - 2024-6, funded by Hong Kong Research Grant Council, General Research Fund

      From the project:
      "Extrapolation describes how machine learning models can be clueless when encountering unfamiliar samples (i.e., samples outside a "convex hull" of their training sets). Consider a young and well-educated immigrant applying for a car loan. An automated system evaluates the loan request. Extrapolation might happen for an applicant because she is an immigrant, relatively young, and very well-educated – the model has not seen any profile of this kind. The model might not make a sound choice because the information about this applicant is not similar to the samples on which the model has been trained. Having a loan officer look over the model's decision would be reasonable. But in the use of AI systems, this common-sense approach is somehow lost, and so far, there is no requirement for AI models to report when they are clueless. How should we measure, classify, and make transparent the extent of AI cluelessness?"
      –"Clueless AI: Should AI Models Report to Us When They Are Clueless?" Montreal AI Ethics. May 19, 2022.

    3. "Monograph-in-progress on media improvisations and informational loss in China, Thriving on Deficits: Aesthetics of Inconspicuous Media Improvisations in China. 

      From the project:
      "Suppose, in a given historical period, the reader-critic of media was a schooled, but by no means erudite, working person of an ordinary household, not, in other words, trained by any disciplinary methodology. How would her experience differ in interacting with print objects and bits of digital information? …In contrast to the oft-celebrated theories of agency, identity, and connectivity crafted for university classrooms, her concerns—modest, often practical, and sometimes irrational and incoherent—can fetch enormous sympathy from people of lesser social standing. Such concerns have rarely been verified: they are felt but not uttered, or uttered in the circumstances not recorded, or recorded yet deemed irrelevant because of their location outside disciplinary and disciplined research…As a result, this reader-critic moves between the conventional categories such as propaganda, literature, and reporting, unbound to any." 
  • 其他
    1. Yale University, Postdoc at MacMillan Center of International and Area Studies
    2. Duke University, Fellow at Brain Cultures Lab
    3. The Chinese University of Hong Kong, Faculty of Arts Outstanding Teaching Award 2023