Culture in the Code: Anthropological Concepts Decoding AI's Hidden Assumptions

Abstract

Contemporary AI development operates on foundational assumptions about intelligence, rationality, and human values that reflect narrow cultural perspectives, limiting alternative conceptions of how AI can be globally beneficial. This paper introduces seven anthropological concepts -- origin story, monoculture, thick description, personhood and community, cargo cult, structuralism, and liminality -- to expose hidden assumptions and offer alternative frameworks for understanding AI alignment. The paper examines how AI systems embody Western-centric notions of rationality as utility optimization, personhood as bounded individuals, and values as quantifiable preferences extracted from behavior. The concepts surface three assumptions that current alignment debates rarely examine: that values can be inferred from behavior, quantified, and formalized; that such quantified values capture what actually matters; and that values, once defined with sufficient precision, are appropriate optimization targets. Against "thin alignment" -- optimization for behavioral proxies stripped of context -- the paper proposes directions toward "thick alignment": recognizing that values emerge through social practice rather than existing as extractable properties; defining more appropriate metrics that capture not only end goals but the conditions that lead to long-term flourishing; and moving from models of fixed identity toward relational ontologies where personhood emerges through social participation. The paper concludes that if AI systems are to create conditions conducive to human flourishing rather than optimize metrics that flatten social experiences, three shifts are needed: redistributing interpretive sovereignty to affected communities, protecting what resists optimization, and extending accountability across generations.