  Growing concerns about the AI alignment problem have emerged in recent years,
with previous work focusing mainly on (1) qualitative descriptions of the
alignment problem; (2) attempting to align AI actions with human interests by
focusing on value specification and learning; and/or (3) focusing on a single
agent or on humanity as a singular unit. Recent work in sociotechnical AI
alignment has made some progress in defining alignment inclusively, but the
field as a whole still lacks a systematic understanding of how to specify,
describe, and analyze misalignment among entities, which may include individual
humans, AI agents, and complex compositional entities such as corporations,
nation-states, and so forth. Previous work on controversy in computational
social science offers a mathematical model of contention among populations (of
humans). In this paper, we adapt this contention model to the alignment
problem, and show how misalignment can vary depending on the population of
agents (human or otherwise) being observed, the domain in question, and the
agents' probability-weighted preferences between possible outcomes. Our model
departs from value specification approaches and focuses instead on the morass
of complex, interlocking, sometimes contradictory goals that agents may have in
practice. We apply our model by analyzing several case studies ranging from
social media moderation to autonomous vehicle behavior. By applying our model
with appropriately representative value data, AI engineers can ensure that
their systems learn values maximally aligned with diverse human interests.
