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UVA Ph.D. Student Helps Shape Virginia’s First AI Legislation
This summer, University of Virginia School of Data Science Ph.D. student Navya Annapareddy stepped into the policy arena as a Commonwealth of Virginia Engineering and Science (COVES) fellow. Her work culminated in testimony before the Joint Commission on Technology and Science (JCOTS), where she presented a comprehensive study on artificial intelligence provenance and labeling—a critical framework for ensuring that AI-generated content is traceable, trustworthy, and clearly disclosed to the public.
The bill study, prepared under JCOTS’ direction, examined legislative options for requiring provenance metadata and labeling of AI outputs. Annapareddy highlighted three key principles—portability, persistence, and tamper-evidence—that would allow provenance data and content labels to survive editing and sharing across platforms while signaling if manipulation has occurred. “The goal,” she told commissioners, “is to give the public confidence in what they are seeing and reading, without locking innovation into one technical standard.”
Her presentation drew from growing industry momentum, including the Coalition for Content Provenance and Authenticity and Adobe’s Content Credentials initiative, while situating Virginia’s efforts within a national patchwork of state-level bills. Annapareddy’s recommendations emphasized starting with the labeling of authentic, trusted content, allowing creators and institutions to certify human-generated material rather than attempting to tag every possible instance of synthetic media.
In addition to her testimony on provenance and labeling, Annapareddy contributed to broader conversations during the September 3 JCOTS full commission meeting, which also reviewed proposals on AI training data regulation and algorithmic pricing devices. The commission’s deliberations reflect Virginia’s growing role in shaping policy around transparency, accountability, and responsible innovation in artificial intelligence.
For Annapareddy, the fellowship provided a platform to apply her data science and machine learning expertise beyond the lab. “AI is advancing rapidly, but public trust depends on clear guardrails,” she said. “Contributing to this policy study showed me how technical knowledge can help lawmakers balance innovation with accountability.”
As a Ph.D. student at UVA, Annapareddy is exploring the technical and ethical dimensions of data science, with a particular interest in how governance frameworks can keep pace with emerging technologies in high-risk decision-making contexts. Her summer in Richmond demonstrates how the School of Data Science’s mission—advancing responsible data science for the common good—extends from research and teaching into real-world policy impact.
