Cultural ideas challenge assumptions in ai alignment and values
Culture in the Code: Anthropological Concepts Decoding AI's Hidden Assumptions
Computers and Society
Summary
AI systems today often rely on narrow ideas about intelligence and values from Western cultures, which may not work well everywhere. The authors use ideas from anthropology to reveal hidden cultural assumptions in how AI is designed to understand and follow human values. They suggest that values are not just fixed things that can be measured but emerge through social interactions and community. Their work argues for changes in AI design that respect diverse cultural views, protect what can't be easily simplified, and consider the impact across generations.
What this means in practice
- •For ai system designers: Design AI alignment processes that include diverse cultural perspectives beyond behavioral proxies for values.
- •For technology policy teams: Create governance frameworks that redistribute decision-making power to communities affected by AI’s value assumptions.
A position paper. It proposes an approach and reports no results.
Authors
Sonja Schmer-Galunder
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.