Jane Castleman
Graduate Student, Stanford University
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I'm a Computer Science PhD student at Stanford University.

My research centers around the social impacts of AI, focusing on two main areas:

  • Designing and executing rigorous evaluations of data-driven systems.
  • Improving the reliability and safety of human-AI systems.
I primarily draw upon methods from machine learning, measurement science, statistics, and social science. Broadly, I hope my work can inform both technical and policy decisions. My work has been published in venues such as ACM FAccT and AIES.

Previously, I graduated from Princeton University, advised by Aleksandra Korolova, working in the Center for Information Technology Policy. I was also fortunate to collaborate with Jonathan Mayer and Lydia T. Liu.

I'm always happy to discuss research ideas or talk to students thinking about applying to graduate school.

News

Publications

  • Measuring Validity in LLM-based Resume Screening
    Jane Castleman, Zeyu Shen, Blossom Metevier, Max Springer, and Aleksandra Korolova
    International Association for Safe & Ethical AI (IASEAI), 2026. Paper
  • Adultification Bias in LLMs and T2I Models
    Jane Castleman and Aleksandra Korolova
    ACM Conference on Fairness, Accountability, and Transparency (FAccT '25), 2025. Paper
  • Missing the Mark: Rethinking Math Benchmarks for LLMs using IRT
    Jane Castleman*, Nimra Nadeem*, Tanvi Namjoshi*, and Lydia T. Liu
    AAAI 2025 Workshop on AI for Education (iRAISE), Spotlight, 2025. Paper * denotes equal contribution.
  • Why Am I Still Seeing This: Measuring the Effectiveness of Ad Controls and Explanations in AI-Mediated Ad Targeting Systems
    Jane Castleman and Aleksandra Korolova
    7th AAAI Conference on AI, Ethics, and Society (AIES '24), 2024. Paper

Misc

Outside of graduate school, I enjoy spending time outside and playing sports. I was a member of the inaugural Varsity Women's Rugby team at Princeton and was selected to the National Intercollegiate Rugby All-Academic team in 2023.