Transparency & Algorithmic Bias,
Data Science,
Mathematics
Mingyan (Iris) Liu is a former AI Policy Intern at the Federation of American Scientists, where she conducted research and produced policy memos on areas of AI governance, privacy, frontier safety, and accountability.
Iris brings hands-on research experience from a National Science Foundation REU, where she trained and evaluated computer vision models, and co-authored research accepted for presentation and publication at IEEE UMECON. She also brings policy experience from OCA National, where she produced legislative one-pagers on AAPI policy priorities used in advocacy with over 20 congressional offices and developed civic engagement materials distributed to 100+ organizational chapters nationwide.
Currently an undergraduate student at Smith College, Iris is pursuing a B.A. in Mathematical Statistics with a minor in Economics.
AI’s high-stakes outputs directly shape American economic, industrial, and technological outcomes. They must not be accepted entirely on the basis of institutional faith rather than verifiable evidence.
Soft law was never meant to be a permanent solution. Treating it as one, and letting the sandcastle stand in for the skyscraper indefinitely, is how we end up with a decade of voluntary commitments and no enforceable accountability to show for it.