Pioneering the causal data science revolution
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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 fair, transparent, robust, and accurate.
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ResearchWe do research on fundamental solutions to real-world AI ethics problems. By working at the intersection of machine learning, causal inference, and system dynamics, we can identify the root causes of algorithmic bias and prevent models from causing illegal discrimination.
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TechnologyWe develop causal AI technology that can help companies large and small gain a strategic advantage over their competition. Our patent-pending techniques may help companies leverage far more data than their competitors, while providing mathematical fairness guarantees.
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PolicyWe provide non-partisan advice to policy makers on both sides of the aisle for how to modernize AI regulations so that they are aligned to society's values. We take a mathematical approach to regulation, focusing on solutions that can actually be implemented in practice.
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EducationWe publish our research to help advance the field of data science. We partner with universities to train the next generation of data scientists, who will be able to combine knowledge from the social sciences with computer science to develop sociotechnical solutions to AI problems.
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