About Me
I am a data scientist and researcher focused on questions at the intersection of machine learning and public policy.
I am currently a Data Science Manager and Algorithmic Justice Specialist at the American Civil Liberties Union (ACLU), where my work focuses on
the civil rights implications of AI and automated systems, including supporting ACLU’s litigation, advocacy, and public education related to artificial intelligence used in various domains. I also regularly publish and presents original research on the impacts of algorithmic systems, and am
an Affiliate at the Berkman Klein Center for Internet and Society.
Previously, I was an Assembly Fellow at the Berkman Klein Center's Institute for Rebooting Social Media and a
Technology Fellow in the U.S. Senate, where I worked for the Senate Judiciary Committee Antitrust Subcommittee through the
TechCongress
fellowship program. I also spent time at
HuggingFace, where I focused on
building and improving model cards for machine learning models, and was previously a data scientist at the Stanford Computational Policy Lab. I hold an M.S. in Management
Science and Engineering (Computational Social Science) and a B.S. in Mathematical and Computational Science, both from Stanford University.
Academic Publications
- Auditing the Audits: Lessons for Algorithmic Accountability from Local Law 144's Bias Audits. With Ro Encarnación,
Cole Tanigawa-Lau, Lena Armstrong, Ana Gutiérrez, and Danaé Metaxa. ACM Conference on Fairness, Accountability, and Transparency (2025) Received an Honorable Mention for Best Paper.
- Challenge Accepted? A Critique of the 2021 National Institute of Justice Recidivism Forecasting Challenge. Led by Tobi Jegede,
with Amreeta Mathai and Aaron Horowitz. ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (2023)
- The Devil is in the Details: Interrogating Values Embedded in the Allegheny Family Screening Tool. With Tobi Jegede,
Tarak Shah, Ana Gutiérrez, Sophie Beiers, Noam Shemtov, Kath Xu, Anjana Samant, and Aaron Horowitz. ACM Conference on Fairness, Accountability, and Transparency (2023)
- The Misuse of AUC: What High Impact Risk Assessment Gets Wrong. With Kweku Kwegyir-Aggrey, Malika Mohan, Aaron Horowitz
and Suresh Venkatasubramanian. ACM Conference on Fairness, Accountability, and Transparency (2023)
- Measuring Data. With Margaret Mitchell, Alexandra Sasha Luccioni, Nathan Lambert, Angelina McMillan-Major, Ezinwanne Ozoani, Nazneen Rajani, Tristan Thrush, Yacine Jernite, Douwe Kiela (2022)
- Empirical Approaches to Identify Systemic Discrimination in Policing. With Alex Chohlas-Wood, Sharad Goel, Aziz Huq, Amy Shoemaker, Ravi Shroff, and Keniel Yao (2022)
- Identifying and Measuring Excessive and Discriminatory Policing. With Alex Chohlas-Wood, Sharad Goel, Aziz Huq, Amy Shoemaker, Ravi Shroff, and Keniel Yao. University of Chicago Law Review, Vol. 89 (2022)
- The Scales of (Algorithmic) Justice: Tradeoffs and Remedies. With Matthew Sun. ACM SIGAI AI Matters Newsletter (2019)
Other Writing
Previous Projects
- As a 2022-2023 Assembly Fellow at the Berkman
Klein Center's Institute for Rebooting Social Media, I built term tabs, a tool for querying definitions of tech-related terms in social media legislation introduced in Congress.
Learn more about term tabs here.
- At Hugging Face, I worked on improving the state of model cards for machine learning models, including a landscape analysis of machine learning documentation tools.
- My work in the Senate focused on legislation, oversight, and other policy efforts related to online safety, algorithmic decision-making, and competition in digital markets.
- In a Stanford Law School Policy Lab, I led a team of law, engineering, and sociology students to develop transparency standards for pretrial risk assessments and presented our findings to the Judicial Council of California.
