Bio
Wanheng Hu is an Embedded Ethics Fellow at Stanford University, jointly appointed by the McCoy Family Center for Ethics in Society, the Institute for Human-Centered Artificial Intelligence (HAI), and the Computer Science Department.
His research lies at the intersection of social studies of science, medicine, and technology; critical data/algorithm studies; media studies; and public engagement with science. His dissertation ethnographically examines the cultivation of credible machine learning models in complex expert practices, with an empirical focus on image-based diagnostics within the Chinese medical AI industry. Another line of his work focuses on the democratic engagement of ordinary citizens in technoscientific affairs, particularly concerning AI development.
Wanheng received his Ph.D. in Science and Technology Studies from Cornell University, where he also completed a minor in Media Studies and remains an active member of the Artificial Intelligence, Policy, and Practice (AIPP) initiative. He is currently an affiliate at the Data & Society Research Institute.
Professional Education
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Ph.D., Cornell University, Science and Technology Studies (Minor: Media Studies) (2024)
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M.Phil., Peking University, Philosophy of Science and Technology (2017)
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B.L., Peking University, Sociology (2014)
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B.Sc., Peking University, Biomedical English (2014)
Contact
https://orcid.org/0000-0002-9701-6089All Publications
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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
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Machine Learning in Medical Systems: Toward a Sociological Agenda
The Oxford Handbook of the Sociology of Machine Learning
Oxford University Press. 2024
View details for DOI 10.1093/oxfordhb/9780197653609.013.28
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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/ -
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