DeepVoting: Learning and Improving Voting Rules with Fine-Tuning

Recently, social choice theory has been found broadly useful for AI alignment and evaluation, often based on the axiomatic properties for combining preferences provided by social choice functions. However, for many sets of axioms, functions which $\text{never}$ violate the axioms are known to not exist, e.g., Arrow's Impossibility Theorem. Recent work on learning novel rules to $\text{minimize}$ axiom violations shows the effectiveness of machine learning but explores a limited class of axioms. In this work we show the effectiveness of standard social choice data structures as features for learning existing voting rules, resulting in networks that have better accuracy than past approaches while using an order of magnitude fewer parameters. Subsequently, we develop a process for $\text{adding axiomatic properties}$ to existing voting rules. By building novel axiomatic loss functions we are able to use transfer learning to fine-tune existing voting rules in a way that quantitatively preserves their original behaviour while also exhibiting strong adherence to previously absent axiomatic properties, resulting in novel voting rules that are $\text{closer}$ to the impossibility frontier than those in the machine learning or theoretical literature.