Building AI systems by aggregating public moral votes doesn't guarantee fairness; the developers' upstream design choices (feature selection, voter composition, question wording) are actually normative decisions that reshape what the public thinks they're voting for.
This paper reveals that moral AI systems trained on public votes aren't neutral—developers' hidden choices about which features to vote on, who votes, and how questions are worded significantly shape the resulting AI behavior.