AI Legislator Algorithm

This project is a sub-project, and part of a larger partnership with Humanity Unleashed to build an AI Legislator. The partnership aims to develop a comprehensive AI-driven framework for understanding, predicting, and proposing policies using multivariate time series and legislative data. It consists of several teams working on different aspects: curating large datasets from US law and time series data, developing value elicitation algorithms for understanding user values and proposing policies, building user-friendly frontends for public policy and legislation analysis, pretraining large foundation models for time-series prediction, designing specialized model architectures, and establishing scaling laws for time-series models. The project seeks to leverage AI and deep learning to bring transparency, efficiency, and effectiveness to policymaking and public policy analysis, ultimately creating an ecosystem where both citizens and legislators can interact with data-driven insights and AI-generated recommendations.

This team will design a sample efficient value elicitation algorithm for understanding what a user values are (political views, morality, hobbies, etc.). Successful value elicitation means being able to predict what a user would prefer between two options in a majority of cases, and doing so efficiently means asking as few questions to the user as possible and elicit as much information as possible about the values of the user. Afterwards, one can attempt to propose policy by finding middle ground amongst the (potentially conflicting) values of several different users. This is done by taking some question (Should abortion be allowed?) and simulating the responses from each user using their values.