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Prof. CAO Xuenan

Prof. CAO Xuenan

Assistant Professor

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

About Prof. CAO Xuenan

Xuenan Cao is Assistant Professor of Cultural Studies in the Department of Cultural and Religious Studies at The Chinese University of Hong Kong. She holds a CUHK direct grant and a GRF grant on AI extrapolation (i.e., how AI goes beyond their training to generate outputs). Her research on AI ethics, digital media, youth cultures, and writings about literature has been published in Big Data & Society (2023), Theory, Culture & Society (2021)Extrapolation (2019), Journal of Language, Literature, and Culture (2016), amongst others. Other works are forthcoming in Oxford Handbook on Digital Studies in China and Days of Future Pasts: Memorializing the Archive . 


She teaches the following courses at CUHK:

MA_AI 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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    • Digital Studies
    • Alternative Media Aesthetics and Theories
    • AI Accountability (Extrapolation)
    • Women Studies
    • Working-Class
    • World Literature
    1. 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.

    2. Monograph-in-progress on informational loss in China, Prints, Information, and AI Gadgets.

      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." 
    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