这篇研究用809人和3个场景，实锤了AI道德投票不中立，开发者的三处选择就能改变结果，做AI治理的别错过。
一项针对道德AI偏好诱导的研究显示，开发者在特征范围、选民抽样、问题框架三个环节的隐性选择会显著改变聚合出的道德偏好。研究分两个阶段，共809名参与者，覆盖AI肾脏分配、AI代理模拟缺勤工人、生成式AI描绘逝者三个场景。结果显示，道德相关特征在不同场景间不可直接迁移；约三分之一特征的偏好随政治意识形态不同而改变，部分方向相反。问题措辞的差异可使意识形态差距扩大或缩小最多一个完整刻度点。研究者认为，仅靠投票聚合无法实现公平透明的AI对齐，每个环节都应被审计和公开。
Participatory Moral AI Is Not Neutral: The Invisible Hand of Developers
As AI systems make more morally loaded decisions across society, one response has been moral preference elicitation. In this approach, researchers poll participants on hypothetical dilemmas and use the aggregated votes to train a policy that an AI model then applies at scale. Before any vote is cast, developers make three key choices in the moral AI elicitation pipeline: feature scoping, voter sampling, and question framing. In other words, they decide which features go to a vote, which voters to include, and how to present the question. These choices are often opaque, undocumented, and treated as technical details rather than normative ones. We examine each of these choices within a common empirical study and show that each can shape the preferences produced by moral AI elicitation. Across two phases (N = 809) in three deployment contexts (i.e., AI kidney allocation, AI agents simulating absent workers, and generative AI depictions of the deceased), we examine the three main stages of the moral AI elicitation pipeline. First, morally relevant features shift across contexts. This suggests that feature schemas should not be assumed to transfer across deployment domains. Second, preferences differ by political ideology for roughly one-third of features, with some differences reversing direction. The ideological composition of the voter pool can therefore affect the resulting aggregated preference profile. Third, the wording of the elicitation question can narrow or widen ideological gaps by up to a full scale point. The framing conditions also change how moral foundations are associated with participants' judgments. Taken together, these findings suggest that voting-based alignment cannot deliver fair or transparent AI by aggregation alone; at minimum, each stage of the moral AI elicitation pipeline should be audited and disclosed.