Generative AI voting: fair collective choice is resilient to LLM biases and inconsistencies

TL;DR

This paper investigates how large language models (LLMs) can serve as AI representatives in voting, revealing their biases, inconsistencies, and potential to enhance fairness and resilience in collective decision-making, especially with abstentions.

Contribution

It introduces a rigorous analysis of over 50,000 LLM voting personas across real elections, highlighting the effectiveness of proportional aggregation methods like equal shares for fairness and resilience.

Findings

Proportional ballot aggregation improves fairness for humans and AI.

Simpler majoritarian elections show higher consistency than complex formats.

AI representatives mitigate voter abstention and enhance democratic resilience.

Abstract

Recent breakthroughs in generative artificial intelligence (AI) and large language models (LLMs) unravel new capabilities for AI personal assistants to overcome cognitive bandwidth limitations of humans, providing decision support or even direct representation of abstained human voters at large scale. However, the quality of this representation and what underlying biases manifest when delegating collective decision making to LLMs is an alarming and timely challenge to tackle. By rigorously emulating more than >50K LLM voting personas in 363 real-world voting elections, we disentangle how AI-generated choices differ from human choices and how this affects collective decision outcomes. Complex preferential ballot formats show significant inconsistencies compared to simpler majoritarian elections, which demonstrate higher consistency. Strikingly, proportional ballot aggregation methods such as equal shares prove to be a win-win: fairer voting outcomes for humans and fairer AI representation, especially for voters likely to abstain. This novel underlying relationship proves paramount for building democratic resilience in scenarios of low voters turnout by voter fatigue: abstained voters are mitigated via AI representatives that recover representative and fair voting outcomes. These interdisciplinary insights provide decision support to policymakers and citizens for developing safeguards and policies for risks of using AI in democratic innovations.