About
Company
Epistamai is an applied research company focused on the intersection of causality and machine learning to solve some of the most difficult problems in AI.
We founded Epistamai to address a fundamental gap in how both academia and industry approach AI ethics. Problems of fairness, bias, and inequality in AI systems require more than better data or bigger models — they require a different way of thinking. We bring together machine learning, causal inference, and systems thinking to develop solutions that are rigorous, interpretable, and aligned with human values.
Story
Epistamai grew out of research conducted at the Federal Reserve studying algorithmic bias in financial systems. That work revealed how standard machine learning techniques, relying on correlation alone, can embed and amplify existing societal inequalities in ways that are difficult to detect and even harder to fix.
In response, we developed a causal Bayesian network modeling approach that makes assumptions explicit, encodes domain knowledge, and enables fairness interventions grounded in causal reasoning rather than statistical proxies. That approach became the foundation of everything we do.
Founder
Chris Lam
CEO
Chris is a Chinese American computer scientist and entrepreneur who is focused on leveraging causality to improve data science. He previously worked at the Federal Reserve Bank of Chicago, Hewlett Packard, and Consumer Reports. Chris earned a BSE in computer science from Penn, an MS in electrical engineering from Columbia, and an MBA from Northwestern (Kellogg).