Artificial Intelligence Research for Fighting Political Polarisation: A Research Agenda

Political polarization is a growing phenomenon in numerous countries around the world. In many cases, this polarization is leading to a divergence between the positions of elected decision makers, creating a loss of middle-ground and compromise, which is in turn causing the democratic process to grind to a halt. The causes of this polarization are both contentious and numerous, with many sociologists, political scientists, economists, and historians weighing in on the matter. Although computer science cannot comment on the causes, we may be able to develop group decision making algorithms and tools to help ameliorate the situation. In this paper we outline some general research proposals on the possibility of combining machine learning techniques with results from computational social choice to mitigate the polarization. Specifically, we focus on the design of multi-winner voting rules that are both efficiently computable and have provable guarantees on desirable properties such as proportionality and representation. We hope that research advances in these areas can have concrete impact on fighting political polarization.