Epistemic Decisions Lab

As impressive new AI capabilities emerge, we need new methods for evaluating, aggregating, and interacting with multiple sources of intelligence, artificial and human.

Statistical foundations for multi-agent decision-making. Models present us with new inputs to important decisions, but how do we pinpoint and design for the complementary strengths of different agents working together on complex decision problems?

Beliefs, explanations, and uncertainty. How do we know what a model knows? How do we teach it to explain itself well? How do we quantify uncertainty in its outputs in principled and human aligned ways?

AI for science. What are the risks to scientific progress of AI-automated production and evaluation of research? How should we formalize the goals of scientific processes such as peer review or discovery-oriented research so as to best leverage the capabilities of AI?

Our lab has received numerous paper awards and PhDs have gone on to tenure track faculty positions at highly ranked CS departments. If you’re obsessed with doing rigorous work on these topics, consider applying to work with the lab.

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