Tomo Lazovich

Breadcrumb

Tomo Lazovich

Assistant Professor of the Practice of Data Science

Barus 203

Areas of Expertise Ethics, Inequality & Poverty, Science & Technology Policy, Technology & Innovation, Technology and Policy

Areas of Interest Technology law and policy, Public benefits programs, Algorithmic harms and redress

Biography

Tomo Lazovich (they/them) is an Assistant Professor of the Practice in AI Governance and Policy at Brown University’s Data Science Institute and a Faculty Fellow at the Brown Watson School for International and Public Affairs. They hold a PhD in Physics from Harvard University and a JD from Northeastern University. Their research sits at the intersection of technical, legal, and policy solutions for AI harms. They serve as the Policy Director for the CNTR AISLE program, an effort to comprehensively map trends in emerging AI legislation across the U.S. They also lead the Benefits Decoded project, which aims to build a database documenting the use of algorithmic systems in state SNAP and Medicaid programs. They teach several courses at Brown, including AI Law and Policy, Fairness in Automated Decision Making, and the Applied Learning Experience capstone project course for Brown’s Online Master’s in Data Science Policy, Governance, and Society.

Research

AI legislation analysis: Leading policy for the CNTR AISLE program, a project to document and analyze emerging AI-related legislation across the U.S.

Algorithms in public benefit programs: Building a database of uses of algorithms in public benefits programs as a way of bringing more transparency and highlighting hidden uses of automated decision systems, focusing initially on state SNAP and Medicaid programs.

Algorithms and due process: Understanding the legal implications of the increased use of algorithms in the government on the fundamental constitutional right to notice under procedural due process

Publications

Lazovich, T.; Williams, L.; Automation in Means-Tested Social Welfare Programs: Challenges and Opportunities, in preparation for Yale Law Journal Forum

Lazovich, T. and the CNTR AISLE team; Mapping the Landscape of AI-related Legislation in the U.S., accepted to APPAM 2026

Lazovich, T.; Borradaile, G.; Confronting the “Medopticon”: Interrogating the Privacy Risks of Pervasive Prescription Surveillance in the Context of Abortion and Gender-Affirming Care, in progress, presented at PLSC 2026

Korver, L.; Lazovich, T.; Reda, S.; Large Language Models in K-12 Education: Alignment with State Curriculum

Standards and Student Personas, under review for AAAI 2027, https://arxiv.org/abs/2606.04846

Shiedlower, I.; Lazovich, T.; Suresh, H.; Booth, S.; Rethinking Robot Ownership: Comparing Public, Corporate, and Private Ownership Models, workshopped at WeRobot 2026, final version accepted to AIES 2026

Ramachandranpilai, R.; Tholeti, T.; Lazovich, T.; Baeza-Yates, R.; Position: Responsible Practices and Model Performance Are Not Competing Goals, ICML 2026, https://icml.cc/virtual/2026/poster/67193/.

Teaching

CS/DATA 1491: Fairness in Automated Decision Making
DATA/IAPA 1250: Artificial Intelligence Law and Policy
DSIO 2030: Applied Learning Experience
DATA 0200: Data Science Fluency