Computer science research on the "alignment" of artificial intelligence (AI) systems treats human values as content to be specified and optimized. Decades of social science scholarship describe values differently, as enacted in ordinary practice, interpreted in social context, and contested across plural orders of evaluation and worth. We argue that the dominant alignment techniques, which we call "thin alignment," convert situated moral or value judgments into one or a few metrics via devices of commensuration such as preference labeling, reward modeling, and consumer ratings. Drawing on philosophical and anthropological accounts of thickness, "thick alignment" instead examines how values take shape, inform the development of AI systems, and become subject to contestation over whose interests they serve. We describe two forms of misalignment in seemingly aligned algorithmic systems that highlight thin alignment: "Halcyon misalignment," which presumes a prior, just equilibrium of technology and values from which a system has departed, and "Mirrored misalignment," which names the risks and harms produced by systems that are aligned in a thin sense yet accurately reproduce an unequal social order. A thick account registers such systems as misaligned. We then trace how researchers have taken up and adapted the concept of thick alignment since it was first proposed in 2023, using these adaptations to refine the concept, and to distill three of its pillars: (1) contextual description that both reflects and constitutes values; (2) interrogation of values across the life cycle of a system; and (3) shared, accountable decision-making through which affected publics help determine which values guide the system. We close by considering implications for AI research and policy.

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