Flagship lab
PDID
Political Deepfakes Incident Database
A cross-disciplinary database of politically salient deepfakes, synthetic media, and researcher-coded incident evidence.
The Political Deepfakes Incidents Database (PDID) is a collection of politically-salient deepfakes, encompassing synthetically-created videos, images, and less-sophisticated `cheapfakes.’ The project is driven by the rise of generative AI in politics, ongoing policy efforts to address harms, and the need to connect AI incidents and political communication research. The database contains political deepfake content, metadata, and researcher-coded descriptors drawn from political science, public policy, communication, and misinformation studies. It aims to help reveal the prevalence, trends, and impact of political deepfakes, such as those featuring major political figures or events. The PDID can benefit policymakers, researchers, journalists, fact-checkers, and the public by providing insights into deepfake usage, aiding in regulation, enabling in-depth analyses, supporting fact-checking and trust-building efforts, and raising awareness of political deepfakes. It is suitable for research and application on media effects, political discourse, AI ethics, technology governance, media literacy, and countermeasures.
AI Governance and PolicyAI EthicsPublic and Elite OpinionPolitical Communication and MediaMisinformation, Deepfakes, and Political CommunicationSurveysSurvey experiments (including conjoint experiments)Field and lab experiments
Current contributors
- Daniel S. Schiff Core Faculty
- Kaylyn Jackson Schiff Core Faculty
- Natalia Bueno Faculty Affiliate
- Christina Walker Faculty Affiliate
- JP Messina Core Faculty
- Yaosheng Xu Graduate Affiliate
- Isabella Mulford Undergraduate Affiliate
- Alaina Hall Undergraduate Affiliate
- Makena Hersh Undergraduate Affiliate
- Aadya Pawar Undergraduate Affiliate
- Narek Abelian Graduate Affiliate
- Ervinas Meija Undergraduate Affiliate
- Huy Nguyen Undergraduate Affiliate