AI 对齐助力数字民主：更公平的选民代表与 LLM 偏见
AI Alignment for Digital Democracy: Fairer Voters Representation against LLM Biases
AI 导读
一项 arXiv 研究考察 LLM 模拟选民时的群体偏见：模型对不同人群投票的还原准确度不一。基于三次美国总统选举数据对比一个闭源与一个开源 LLM，去除性别可将最大群体误差降低 22-42%，去除教育信息可降低 64%，但去除自我报告的种族会使平均群体误差增加超过 40%，且均不损失整体准确度。重复查询、公平性指令等常见对齐策略无法降低这些误差，甚至可能使其翻倍。
正文 · 原文
Abstract:AI representation is emerging in digital democracy, assisting in voter emulation and decision-making. Large language models (LLMs) behave differently depending on the model, the prompt context, and the voter information they receive, representing some groups more accurately than others and potentially making the emulation unfair to certain groups. We investigate demographic biases in LLM-emulated electorates: how accurately LLMs represent individual votes, whether some voter groups are represented more accurately than others, and whether AI alignment can reduce these disparities without sacrificing overall accuracy. Using three U.S. presidential elections, we compare a closed-source and an open-source LLM across voters' demographic features and test AI alignment strategies toward fairer voter representation. Removing gender from the voter profile reduces the largest group error of the models by 22-42%, and removing education reduces it by 64%, whereas removing self-reported race increases the mean group error by over 40%, all without lowering accuracy. Common alignment strategies, such as repeated queries or fairness instructions, do not reduce these errors and can double them.
| Subjects: | Computers and Society (cs.CY); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.32780 [cs.CY] |
| (or arXiv:2610.32780v1 [cs.CY] for this version) | |
| https://doi.org/10.48550/arXiv.2610.32780 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yi Qi [view email]
[v1]
Fri, 23 Oct 2026 15:27:40 UTC (1,259 KB)
来源：arXiv cs.AI · arxiv.org