  Scaling up deliberative and voting participation is a longstanding endeavor
-- a cornerstone for direct democracy and legitimate collective choice. Recent
breakthroughs in generative artificial intelligence (AI) and large language
models (LLMs) provide unprecedented opportunities, but also alerting risks for
digital democracy. AI personal assistants can overcome cognitive bandwidth
limitations of humans, providing decision support capabilities or even direct
AI representation of 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 with high realism more than >50K LLM voting personas in 81
real-world voting elections, we show that different LLMs (GPT 3, GPT 3.5, and
Llama2) come with biases and significant inconsistencies in complex
preferential ballot formats, compared to simpler and more consistent
majoritarian elections. Strikingly, fair voting aggregation methods, such as
equal shares, prove to be a win-win: fairer voting outcomes for humans with
fairer AI representation. This novel underlying relationship proves paramount
for democratic resilience in progressives scenarios with low voters turnout and
voter fatigue supported by AI representatives: abstained voters are mitigated
by recovering highly representative voting outcomes that are fairer. These
insights provide remarkable foundations for science, policymakers and citizens
in explaining and mitigating AI risks in democratic innovations.
