Quantifying Misalignment Between Agents

Growing concerns about the AI alignment problem have emerged in recent years, with previous work focusing mostly 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 either a single agent or on humanity as a singular unit. However, the field as a whole 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. Prior 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 viewing misalignment can vary depending on the population of agents (human or otherwise) being observed as well as the domain or "problem area" in question. 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 discuss the implications of our model and leave more thorough verification for future work.