R. Stuart Geiger

R. Stuart Geiger is an Assistant Professor at the University of California, San Diego with joint appointments in the Department of Communication and the Halıcıoğlu Data Science Institute. He holds affiliate faculty roles in Computer Science & Engineering, Computational Social Science, and the Institute for Practical Ethics, where he leads the Working Group on Data Governance & Accountability. His interdisciplinary work spans Science and Technology Studies, Data Science, and AI Ethics.

Geiger earned his Ph.D. in Information Management & Systems from UC Berkeley, an M.A. in Communication, Culture, and Technology from Georgetown University, and a B.A. in Humanities from the University of Texas at Austin. His postdoctoral research at Berkeley Institute for Data Science (BIDS) focused on ethnographic studies of data science practices and institutions.

Research Interests:
His research examines the social, cultural, and political dimensions of data science, AI, and automation. Key areas include algorithmic fairness, open-source software governance, Wikipedia’s socio-technical dynamics, and the ethics of AI in social media moderation. He employs mixed methods, blending ethnography, trace analysis, and computational techniques.

Publications Trends:
Recent work critiques algorithmic bias in AI systems (e.g., ChatGPT’s salary negotiation advice), advocates for participatory auditing practices, and explores labor dynamics in open-source projects. His studies often bridge technical and social dimensions, emphasizing contextual inquiry and institutional critiques.

Awards & Grants:
- Best presentation award at ECSCW 2018
- 1st runner-up David B. Martin Best Paper Award (2018)
- Sloan Foundation grants for open-source software studies
- UC Humanities Research Institute funding (2022)
- Top paper award at 2009 Science & Technology in Society Conference

Advising & Grants:
Geiger has led grants totaling over $200,000, including projects on open-source maintainers’ labor and data science ethics. He advises students in data science, communication, and STS, fostering interdisciplinary collaboration. His teaching includes courses on data ethics, algorithmic auditing, and communication theory.

Labs & Teams:
He co-founded the Best Practices in Data Science series at BIDS and collaborates with teams like the Moore-Sloan Data Science Environments. His work intersects with initiatives on diversity in data science and reproducible research practices.