Traditional data science approaches rely on correlation, which lead to black boxes that humans cannot understand. By applying causal techniques to machine learning, we can build AI systems that are more accurate, transparent, and fair.
We do research on fundamental solutions to real-world AI ethics problems by combining machine learning, causal inference, and systems thinking.
Our patented AI technology helps companies leverage their data while providing mathematical guardrails around AI systems — delivering competitive advantage without sacrificing transparency or fairness.
We provide non-partisan advice to policy makers on modernizing AI regulations through mathematical approaches to implementation — building frameworks that are rigorous, practical, and technology-forward.
We publish research and partner with universities to train the next generation of data scientists in sociotechnical solutions — equipping them to build AI systems that serve society responsibly.