Ought is somewhere between a research lab and a startup.

We do conceptual and empirical research on supporting deliberation using machine learning, and share our findings openly.

This research is guided by a vision for tools and applications that we hope will eventually help millions of people think through the questions and choices they face every day.

We're incorporated as a non-profit, so we can afford to take the long view.


Our focus is on mechanisms for training ML algorithms to answer questions in cases where it's difficult or impossible to get empirical feedback on the quality of the answers.

Can we solve difficult problems by assembling small and mostly context-free contributions from individual agents who don't know the big picture?


In the past, we've explored the following directions:

Can ML algorithms make well-calibrated predictions about human judgments after deliberation based on cheap signals such as multiple people's quick guesses?
What mechanisms could enable users to delegate cognitive work to crowds of human and machine experts? What could applications look like?