This project is building a database that includes survey questions about AI attitudes, opinions, and policy preferences across polls and surveys run by academic institutions, think tanks, and news organizations. This will assist in understanding how AI attitudes vary over time, across countries, and across subgroups of the population
This will assist in understanding how AI attitudes vary over time, across countries, and across subgroups of the population (e.g., the public, experts, policymakers).
The plan is to use this information to conduct meta-analyses, to validate survey questions/instruments, to develop a public-facing tool (like the Roper iPoll) for accessing survey questions and associated data related to AI, and to develop an annual survey on AI attitudes.
An ambitious project focused on reducing carbon footprint through innovative recycling methods.
AI related perceptions are the target of this project.
Faculty:
Graduate researchers: Chloe Ahn, Indira Patil, Anjali Bhati, Yunzhe "Leo" Liu, Maheshwari "Maahi" Das, Maxwell Kowalski, Kunal Rustagi
Undergraduate researchers:
This project is ideal for students who want to build expertise in survey design, survey experiments, and public opinion research. Students will contribute to large-scale meta-analysis and develop technical skills in synthesizing cross-national datasets for policy-relevant questions.