Computational Perspectives on Democracy in the Age of AI

Abstract

Humanity is at a precipice: democracy is declining from a historical peak as backsliding occurs around the world, while artificial intelligence is emerging as a revolutionary technology that could permanently upend the socio-economic fabric underpinning modern society. This thesis presents four pieces of original research as part of a collective attempt to build upon our understanding of democratic processes, especially as democracies around the world adapt to the age of AI. In the following chapters, we (1) measure persistent non-response bias in a well-calibrated nationwide poll for the US Presidential Election, finding that the five largest states saw a >99% reduction in effective sample size, (2) extend a theoretical measure of polarization to participatory budgeting, finding that a newly proposed measure degenerates to a well-known measure ~99.4% of the time when applied to real elections, (3) adapt proportional veto core to the setting with infinite candidates (heralded by generative AI) as a measure of bridging, finding a strong pattern that some voting rules consistently perform better in bridging, and (4) demonstrate that innocuous-looking datasets that are easy to produce can poison AI models to have partisan leanings even after heavy semantic filtering, and develop attribution techniques that reduce subliminal effects by 100% when used for filtering.