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.

翻译：生成式人工智能（AI）与大型语言模型（LLM）的最新突破揭示了AI个人助理的新能力，能够克服人类认知带宽的限制，在大规模场景中为人类提供决策支持，甚至直接代表弃权选民。然而，这种代表的质量以及将集体决策委托给LLM时显现的潜在偏见，是一个亟待解决的紧迫挑战。通过在363场真实世界投票选举中严格模拟超过5万个LLM投票角色，我们揭示了AI生成的选择如何与人类选择相异，以及这如何影响集体决策结果。复杂的优先排序投票格式与较简单多数制选举相比表现出显著的不一致性，而后者展现出更高的一致性。引人注目的是，比例性投票聚合方法（如等额份额法）被证明是一种双赢方案：既为人类带来更公平的投票结果，也为AI代表提供更公平的代表性，尤其适用于可能弃权的选民。这种新颖的底层关系对于在选民疲劳导致低投票率的情形下构建民主韧性至关重要：通过AI代表恢复具有代表性和公平性的投票结果，从而缓解选民弃权问题。这些跨学科见解为政策制定者和公民提供了决策支持，有助于制定针对民主创新中AI使用风险的保障措施与政策。