Xuenan Cao is Assistant Professor of Cultural Studies in the Department of Cultural and Religious Studies at The Chinese University of Hong Kong. Her research on media studies, digital STS, youth cultures, and Chinese literature has been published in Big Data & Society (2023), Theory, Culture & Society (2021), Extrapolation (2019), Journal of Language, Literature, and Culture (2016), amongst others. Before coming to CUHK, she also taught at New York University (Shanghai) and Yale University.
Her approach to culture is two-fold. The first can be summed up by media theorist Marshall McLuhan's law of bibliography: the more ubiquitous objects are, the less likely they will enter our archives; the more there were, the fewer there are. Second, she shifts the narrow focus of communication studies and media theory by drawing on the richness of visual and material cultures in East Asia.
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.
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."