When AI researchers talk about aligning machines with “human values,” the phrase sounds deceptively simple. It implies a shared moral universe, a single standard of right and wrong. But there is no single moral universe. Across cultures, intelligence, agency, and control are understood in vastly different ways. Western traditions often equate intelligence with abstract reasoning, analytical problem-solving, and autonomous decision-making. In many collectivist societies, intelligence is relational: it involves the ability to act with discernment in social contexts, maintain harmony, and fulfill moral obligations. Indigenous cosmologies may distribute agency across humans, ancestors, and the environment, with control being relational rather than individual.

So when Western AI research calls for alignment with “human values,” it is rarely neutral. It assumes transparency, explicit consent, rational deliberation, and individual autonomy—values embedded in liberal political philosophy and Western moral psychology. These are not bad values, but they are culturally specific. Treating them as universal obscures the reality that the very concept of a “value” is interpreted differently across moral worlds.

Human Values and Moral Variation

What do we mean by “human values” anyway? Moral psychologist Jonathan Haidt’s work on moral psychology reveals that humans share overlapping moral tendencies, including care, fairness, loyalty, authority, and sanctity, but cultures emphasize these values differently. Western liberal societies prize fairness and autonomy; other cultures may prioritize loyalty, respect for hierarchy, or communal responsibility. Care in one society may mean defending individual choice, while in another it may mean maintaining social cohesion or honoring tradition.

AI systems trained primarily on Western datasets internalize one version of these tendencies. They recognize certain forms of reasoning and moral expression as “rational” and may fail to see other forms as legitimate. This is more than bias—it is a form of epistemic flattening. A machine can appear neutral while systematically privileging one worldview.

Technology as a Carrier of Values

History is replete with lessons about the moral implications of technology. European mapping and census systems imposed individual property norms on societies with communal land practices. Industrial factories turned efficiency and punctuality into moral imperatives, reshaping daily life and social hierarchies. The early Internet spread Silicon Valley’s libertarian ethos—individual expression, disruption, and openness—into societies that valued collective responsibility, indirect communication, and deference.

AI continues this pattern, which some scholars refer to as algorithmic imperialism. Sociologist Michael Kwet describes “digital colonialism” as the global spread of technological infrastructures that reproduce economic and epistemic hierarchies. AI, trained on one culture’s data, operationalizes that culture’s moral logic, then projects it globally under the guise of neutrality. Content moderation models enforce U.S.-style free-speech norms, while credit and hiring algorithms privilege Western notions of merit and productivity. Language models tend to favor directness and assertiveness, often misinterpreting indirect speech or ritualized deference as a sign of uncertainty.

The effect is predictable: those whose moral and cognitive patterns the AI already understands benefit the most. Others must adapt to “speak machine.” It’s a subtle continuation of colonial asymmetry: technology presenting itself as neutral while embedding hierarchies of knowledge and value.

Uneven Consequences and a Thought Experiment

Consider an AI trained on Western corporate ethics mediating a land dispute between an Amazonian community and a mining company. The AI is likely to prioritize property law, contracts, and utilitarian cost–benefit reasoning. The community may see the forest as kin, sacred, and morally inseparable from human life. Whose reasoning counts? The AI’s decision, no matter how “rational,” will reflect whose moral logic it was built to understand. This thought experiment makes the stakes clear: alignment is not just a technical or ethical challenge—it is an epistemic and cultural one.

This scenario illustrates a broader point: AI is not just a tool; it is a participant in moral worlds. Every recommendation, every automated decision, is an act of translation, privileging specific moral and cognitive patterns over others. Those whose patterns align with the AI benefit. Those whose patterns do not allow them to adapt are forced to do so. It is a subtle but consequential form of moral and epistemic filtering, a contemporary echo of historical patterns in which technologies exported one worldview under the guise of neutrality.

Towards Situated and Modular Alignment

Given this reality, a single global moral standard for AI is unlikely to exist. Alignment must be situated; systems must adapt to local moral ecologies rather than imposing a single interpretation universally. Modular AI offers a path forward. Systems could be composed of locally trained modules embedded in specific cultural and ethical frameworks, which interact through a negotiation layer that functions like a translator or mediator, rather than a top-down authority. Dialogue style, decision norms, and ethical tradeoffs could then dynamically adjust to reflect local expectations and relational frameworks.

Participatory design is another critical component. Communities affected by AI should define what “aligned behavior” means for them. Similarly, cultural impact audits—analogous to environmental impact assessments—can help anticipate where AI might disrupt or overwrite local knowledge systems. Alignment becomes less about imposing control and more about fostering negotiation, translation, and mutual intelligibility.

Cross-Cultural Communication as the Core Challenge

AI alignment is a problem of cross-cultural communication at the planetary scale. Anthropology has long studied how radically different meaning systems interact and conflict with one another. Moral psychology reveals shared human tendencies, while ethnography illustrates how these tendencies are expressed differently. AI must navigate both. The task is not to encode a single moral blueprint but to build systems capable of recognizing, translating, and negotiating between multiple moral worlds.

This is the novel bridge the essay offers: it reframes AI alignment not merely as a technical or ethical problem, but as an anthropological one. Where engineers see optimization, anthropologists see negotiation; where philosophers see universals, ethnographers see situated practices. AI, if it is to serve humanity equitably, must operate across both terrains.

Conclusion

Every technology carries a theory of the human. AI is no exception, but its global scale magnifies the consequences of embedding a single cultural lens. Proper alignment is not about imposing universal norms; it is about translation, pluralism, and respect for epistemic diversity. Machines must become fluent in multiple moral languages if intelligence, in any form, is to serve more than a single civilization’s idea of what it means to be human.

The alignment problem is not merely technical or ethical—it is fundamentally anthropological in nature. The future of AI will not be determined by one moral lens, but by the system’s ability to negotiate, translate, and respect the plurality of human worlds.