AI preference prediction and policy making

Abstract

Democratic decision-making is difficult. Representatives often fail to represent the preferences of their constituents, and directly consulting members of the public can be costly. Inspired by these difficulties, several scholars have discussed the use of artificial intelligence (AI) models to support democratic decision-making. One such particular application is the use of AI to represent public policy preferences by predicting them. In this paper, we perform an analysis on the different ways AI models can be used to represent public policy preferences. We make distinctions between using AI as epistemic tools and for procedure; group and individual predictions; and predictions about preferences and inferences about values. We also describe how AI models can help policymakers screen policies for potential worries and objections, double-check any beliefs they have about the acceptability of their policies, and justify policy proposals. We also consider a number of worries about the use of AI in policymaking. We argue that these worries, while legitimate, can be mitigated or avoided in the way we have proposed the use of AI.