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.

通过对超过 50K 个大型语言模型 (GPT 3、GPT 3.5 和 Llama2) 在 81 个现实世界投票选举中的高逼真模拟，我们展示了不同大型语言模型在复杂的偏好选举格式中存在偏见和显著的不一致性，与简单且一致的多数选举相比。公平的投票聚合方法，如均等份额，被证明是双赢的：对人类来说更公平的投票结果与更公平的人工智能代表相结合。这一新颖的潜在关系对于在低投票率和由人工智能代表支持的进步场景中的民主韧性至关重要：通过恢复高度代表性且更为公平的投票结果，可以缓解棄權选民的问题。这些见解为科学家、政策制定者和公民提供了在解释和缓解民主创新中的人工智能风险方面的重要基础。