Evaluation science for high-stakes AI.
PSAIL's research asks a single question in four different ways: can this AI system be trusted where the cost of error is a wrongful outcome?
Methods across high-stakes domains.
The four programs are method axes, not domains. PSAIL proves them first in public safety, then extends them to adjacent settings where the cost of error is just as high — including national and economic security.
Methods, projects, impact.
One page, from foundations to impact: data and legal constraints feed four method programs, each program drives a project, and each project produces its own papers, protected methods, and real-world impact — across public-safety and security domains.
How we work.
Publish on acceptance
Papers appear here only once accepted. Work under review or in preparation is marked in progress — never dressed up as published.
Disclose after filing
Inventions stay confidential until filed. The Research Map shows the method stage, but never unfiled claims or content.
Validate against ground truth
Synthetic populations are calibrated to national statistics before use, and evaluation is anchored to human-expert baselines.