Key papers include work on disparate impact in machine learning and accelerating materials discovery with interpretable machine learning. Received grants for her work on fairness in machine learning, fairness and social networks, using interpretable machine learning techniques to inform scientific hypotheses, responsible CS education, and policy and discriminatory machine learning.
Research Experience
Previously served as the Assistant Director for Data and Democracy in the White House Office of Science and Technology Policy under the Biden-Harris Administration, where she co-authored the AI Bill of Rights and contributed to policy governing the federal use of AI. Worked as a software engineer at Alphabet (formerly Google), in the X lab and search infrastructure.
Education
Ph.D. in Computer Science from the University of Maryland, College Park; B.A. from Swarthmore College.
Background
Shibulal Family Professor of Computer Science at Haverford College, a Nonresident Senior Fellow at The Brookings Institution, and the Chair of ACM's U.S. Technology Policy Committee. Her research focuses on the fairness and interpretability of machine learning.
Miscellany
Co-Founder of the ACM Conference on Fairness, Accountability, and Transparency (FAccT). Research interests also include responsible computer science education and policy and discriminatory machine learning.