The Stanford Trustworthy AI Research (STAIR) Lab works to develop principles and practices of trustworthy machine learning, including fairness and robustness. Our applied research includes applications to healthcare and neuroimaging.
Research
Our research interests span three interconnected areas:
AI Measurement Science
How do we know if AI systems are performing well, behaving fairly, or meeting safety requirements? We develop principled approaches to measuring AI capabilities, limitations, and impacts. This includes work on evaluation metrics, benchmarking methodologies, and understanding what our measurements actually tell us about AI systems.
Trustworthy AI and Society
We study the foundations of building AI systems that are reliable, fair, and aligned with human values. Our work addresses:
- Fairness: Developing algorithms and metrics for equitable AI
- Robustness: Making AI systems reliable under distribution shift and adversarial conditions
- Privacy: Protecting sensitive information in machine learning pipelines
- Federated Learning: Training models across distributed data while preserving privacy and security
Applications and Real-World Impact
We apply our research to high-stakes domains including:
- Healthcare: Clinical prediction models, medical imaging analysis
- Neuroscience: Understanding brain function through fMRI and cognitive modeling
- Scientific Discovery: Accelerating research through reliable AI tools
Recent News
Check out our publications and team members.