Aggregation Problems in Machine Ethics and AI Alignment

Artificial agents increasingly make decisions with far-reaching consequences. It is therefore imperative to ensure that their actions are not only functionally effective but also normatively appropriate. Two major paradigms address this challenge: machine ethics and value alignment. Machine ethics typically engages in \textit{moral} aggregation, especially through value and (descriptive) uncertainty aggregation. Value alignment approaches tend to rely on \textit{social} aggregation to manage value pluralism and moral uncertainty, often implicitly or indirectly. This paper disentangles these forms of aggregation and analyzes their roles across three stages of machine moral reasoning: moral evaluation, moral assessment, and moral decision. Rather than favoring one paradigm, we expose their mutual dependencies and respective blind spots, particularly under conditions of persistent moral disagreement. We argue that social aggregation cannot bypass deep normative commitments. Alignment by social aggregation cannot replace moral aggregation but merely relocates it---often opaquely.