Machine Learning for Socio-technical Systems Lab
The Machine Learning for Socio-technical Systems Lab (ML4STS) at the University of Rhode Island is directed by Dr. Sarah M Brown, Assistant Professor of Computer Science. The lab takes a socio-technical approach to ML research, applying data science to understand the world with other scientists, studying and evaluating ML systems directly, and building tools to help data scientists employ best practices. Much of the lab's work focuses on fairness of automated decision-making systems, including developing benchmarks for fair data-driven decision-making with LLMs, building evaluation tools for ML fairness interventions, and collaborating with social scientists on perceptions of algorithmic fairness.
Funding Details
- Annual Budget
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- Current Runway
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- Funding Goal
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- Funding Raised to Date
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- Fiscal Sponsor
- University of Rhode Island Foundation & Alumni Engagement
Theory of Change
The ML4STS Lab works to reduce harms from automated decision-making systems by developing practical tools, benchmarks, and frameworks that help data scientists identify and prevent unintended systemic errors before deployment. By studying fairness at the task level, building evaluation benchmarks for LLM-based decision-making, and creating interventions informed by both technical and social science perspectives, the lab aims to ensure ML systems do not reinforce patterns of discrimination. The lab also develops AI safety curricula for students, helping train the next generation of technologists to anticipate potential societal impacts of AI systems during the design phase rather than after deployment.
Grants Received
from Survival and Flourishing Fund
Projects
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Details
- Last Updated
- Mar 19, 2026, 7:10 PM UTC
- Created
- Mar 18, 2026, 11:18 PM UTC