Tax policy analysis is a well-developed field with a robust body of research and extensive modeling infrastructure across think tanks and government agencies. Because tax policy affects everyone, and especially wealthy people, it gets both a lot of attention and research funding (notably from individual foundations like those of Peter G. Peterson and Koch brothers). In addition to empirical studies, organizations like the Urban-Brookings Tax Policy Center and the Joint Committee on Taxation produce microsimulations of tax policy to comprehensively model thousands of levers of policymaking. However, because it is difficult to guess how people will react to changing public policy scenarios, these models are limited in how much they account for individual behavioral factors. Although it is far from certain, artificial intelligence (AI) might be able to help address this notable deficiency in tax policy, and recent work has highlighted this possibility.
A team of researchers from Harvard and Salesforce developed an AI system designed to propose new tax policies, which they call the AI economist. While the results of their initial analysis are not destined for the U.S. Code of Law, the approach they are proposing is potentially quite meaningful. Most current tax policy models infer how people would respond to a change in policy based on the results of prior research. In the AI economist approach, though, the actions of the computational economic participants were instead learned from a simplified game economy. They did this using a type of AI called reinforcement learning.
In pretty much any social-good application, AI does nothing on its own. However, with prudent application by domain experts, AI can lead to incremental improvements that, over time, have meaningful impact—as is true in policy research. Economists Susan Athey and Guido Imbens write “though the adoption of [machine learning] methods in economics has been slower, they are now beginning to be widely used in empirical work.” They are referring to machine learning methods for econometrics questions (such as causal inference), and less so simulations, but it’s possible that too will change over time.
The original article can be found here.
In the field of causal inference, Professor Judea Pearl is a pioneer for developing a theory of causal and counterfactual inference based on structural models. In 2011, Professor Pearl won the Turing Award, computer science’s highest honor, for “fundamental contributions to artificial intelligence through the development of a calculus of probabilistic and causal reasoning.” In 2020, Professor Pearl is also awarded as World Leader in AI World Society (AIWS.net) by Michael Dukakis Institute for Leadership and Innovation (MDI) and Boston Global Forum (BGF). At this moment, Professor Judea also contributes to Causal Inference for AI transparency, which is one of important AIWS.net topics on AI Ethics.