Large language models (LLMs) have shown promising accuracy in predicting survey responses and policy preferences, which has increased interest in their potential to represent human interests in various domains. Most existing research has focused on behavioral cloning, effectively evaluating how well models reproduce individuals' expressed preferences. Drawing on theories of political representation, we highlight an underexplored design trade-off: whether AI systems should act as delegates, mirroring expressed preferences, or as trustees, exercising judgment about what best serves an individual's interests. This trade-off is closely related to issues of LLM sycophancy, where models can encourage behavior or validate beliefs that may be aligned with a user's short-term preferences, but is detrimental to their long-term interests. Through a series of experiments simulating votes on various policy issues in the U.S. context, we apply a temporal utility framework that weighs short and long-term interests (simulating a trustee role) and compare voting outcomes to behavior-cloning models (simulating a delegate). We find that trustee-style predictions weighted toward long-term interests produce policy decisions that align more closely with expert consensus on well-understood issues, but also show greater bias toward models' default stances on topics lacking clear agreement. These findings reveal a fundamental trade-off in designing AI systems to represent human interests. Delegate models better preserve user autonomy but may diverge from well-supported policy positions, while trustee models can promote welfare on well-understood issues yet risk paternalism and bias on subjective topics.

翻译：大语言模型（LLM）在预测调查回应和政策偏好方面已展现出令人期待的准确性，这增强了人们对其在不同领域代表人类利益的潜力的兴趣。现有研究大多集中于行为克隆，有效评估了模型在多大程度上复现了个体表达出的偏好。借鉴政治代表理论，我们强调了一个尚未被充分探索的设计权衡：人工智能系统应作为代表（镜像反映表达出的偏好），还是应作为受托人（行使判断以服务于个体的最佳利益）。这一权衡与LLM的谄媚性问题密切相关，即模型可能鼓励某些行为或认可某些信念，这些行为或信念可能与用户的短期偏好一致，却损害其长期利益。通过一系列模拟美国背景下各类政策议题投票的实验，我们应用了一个权衡短期与长期利益的时间效用框架（模拟受托人角色），并将投票结果与行为克隆模型（模拟代表角色）进行比较。我们发现，倾向于长期利益的受托人式预测在共识明确的议题上产生的政策决定更符合专家共识，但在缺乏明确共识的议题上也表现出对模型默认立场的更大偏见。这些发现揭示了设计代表人类利益的人工智能系统时的一个根本权衡：代表模型能更好地维护用户自主性，但可能与有充分依据的政策立场相偏离；而受托人模型能在共识明确的议题上促进福祉，却在主观议题上存在家长式作风和偏见的风险。