Bio
Wanheng Hu is a scholar of Science and Technology Studies (STS) whose research examines the epistemic, ethical, and regulatory dimensions of artificial intelligence, with a particular focus on machine learning in medicine. His current book project, Reassembling Expertise: Credible Knowledge and Machine Learning in Medical Imaging, is an ethnographic study of the Chinese medical AI industry. Drawing on multi-sited fieldwork, the project analyzes how, and in what sense, human medical expertise is translated into AI systems and how the credibility of these systems is negotiated across industrial, clinical, and regulatory settings. His broader scholarship engages the social studies of science, medicine, and technology; the sociology of expertise; critical data and algorithm studies; media studies; and public engagement with science.
Wanheng is currently an Embedded Ethics Fellow at Stanford University’s McCoy Family Center for Ethics in Society, in partnership with the Institute for Human-Centered Artificial Intelligence (HAI) and the Department of Computer Science. He is also an affiliate of the Data & Society Research Institute, a member of the Schwartz Reisman Institute’s AI & Trust Working Group at the University of Toronto, and a member of Cornell University’s Artificial Intelligence, Policy, and Practice (AIPP) initiative. He was previously a Fellow at Harvard Kennedy School’s Program on Science, Technology and Society (2022–23). He holds a Ph.D. in STS with a minor in Media Studies from Cornell University. His research has been supported by the U.S. National Science Foundation, the China Times Cultural Foundation, and Cornell’s Hu Shih Fellowship, among other sources, and has appeared in venues including Public Understanding of Science and The Oxford Handbook of the Sociology of Machine Learning.
Professional Education
-
Ph.D., Cornell University, Science and Technology Studies (Minor: Media Studies) (2024)
-
M.Phil., Peking University, Philosophy of Science and Technology (2017)
-
B.L., Peking University, Sociology (2014)
-
B.Sc., Peking University, Biomedical English (2014)
Contact
https://orcid.org/0000-0002-9701-60892024-25 Courses
All Publications
-
Machine Learning in Medical Systems: Toward a Sociological Agenda
The Oxford Handbook of the Sociology of Machine Learning
Oxford University Press. 2025; Print: 483–508
View details for DOI 10.1093/oxfordhb/9780197653609.013.28
-
Imagining the model citizen: A comparison between public understanding of science, public engagement in science, and citizen science
PUBLIC UNDERSTANDING OF SCIENCE
2024; 33 (6): 709-724
Abstract
This article examines the visions of citizens' ideal practices regarding technoscientific affairs in a democratic society, namely "imaginaries of model citizens," that underlie three science and public initiatives: public understanding of science, public engagement in science, and citizen science. While imaginaries of citizens are performative and necessary to these initiatives, they are often relegated to the background. I argue that such imaginaries are the result of a complex of perceptions on the nature of science, the role of democracy in scientific activities, and the form of "democratizing" science. The imaginary of model citizens in public understanding of science is of literate citizens who should know science sufficiently, use it in daily life, and support science; in public engagement in science, the model citizen is a responsible one who should engage in the governance of technoscientific issues; and in citizen science, a contributive one who should partake in and enjoy creating scientific knowledge.
View details for DOI 10.1177/09636625241227081
View details for Web of Science ID 001166417200001
View details for PubMedID 38369701
-
Enrolling Citizens: A Primer on Archetypes of Democratic Engagement with AI
Data & Society Research Institute.
NY: New York.
2024
15
Abstract
In response to rapid advances in artificial intelligence, lawmakers, regulators, academics, and technologists alike are sifting through technical jargon and marketing hype as they take on the challenge of safeguarding citizens from the technology’s potential harms while maximizing their access to its benefits. A common feature of these efforts is including citizens throughout the stages of AI development and governance. Yet doing so is impossible without a clear vision of what citizens ideally should do. This primer takes up this imperative and asks: What approaches can ensure that citizens have meaningful involvement in the development of AI, and how do these approaches envision the role of a “good citizen”? The primer highlights three major approaches to involving citizens in AI — AI literacy, AI governance, and participatory AI — each of them premised on the importance of enrolling citizens but envisioning different roles for citizens to play. While recognizing that it is largely impossible to come up with a universal standard for building AI in the public interest, and that all approaches will remain local and situated, this primer invites a critical reflection on the underlying assumptions about technology, democracy, and citizenship that ground how we think about the ethics and role of public(s) in large-scale sociotechnical change.
https://datasociety.net/library/enrolling-citizens-a-primer-on-archetypes-of-democratic-engagement-with-ai/ - Thinking Machine Intelligence through Human Actions: An Interview with Anthropologist Lucy Suchman [通过人类行动思考机器智能——专访人类学家露西·萨奇曼] Journal of Intelligent Society 2024; 3 (1): 198-215
-
Beyond Technonationalism: Biomedical Innovation and Entrepreneurship in Asia (Book Review)
JOURNAL OF DEVELOPMENT STUDIES
2023; 59 (6): 955-957
View details for DOI 10.1080/00220388.2023.2175444
View details for Web of Science ID 000936595800001
- Review of Pelillo, Marcello; Scantamburlo, Teresa. Machines We Trust: Perspectives on Dependable AI H-Sci-Med-Tech, H-Net Reviews. 2022
- Seeing Like STS: The 3-S Problems of the Co-Production of Science and Social Order [如何“从STS的视角看”？关于科学与社会秩序之“共同生产观”的几个问题] Tsinghua Sociological Review 2020; 14: 21-45