General Scales Framework Reveals AI's Cognitive Limits and Structural Risks in Automated Evaluation Systems
Original framing: “General scales unlock AI evaluation with explanatory and predictive power” — Nature
Cross-culturally, intelligence is not a monolithic construct but a pluralistic one, with non-Western traditions emphasizing contextual, ethical, and relational dimensions of cognition. For instance, Confucian traditions prioritize 'practical wisdom' (zhi) over abstract reasoning, while Hindu philosophy frames intelligence as a means to 'liberation' (moksha), not market productivity. The study's rubric-based approach, which rewards tasks like 'logical deduction' or 'multi-step reasoning,' implicitly privileges Western academic norms over Indigenous or Eastern epistemologies. This cultural myopia risks producing AI systems that perform well in Western contexts but fail in diverse cultural settings.
The study's rubric-based AI evaluation framework exemplifies the techno-solutionist paradigm that prioritizes scalability and predictability over ethical and cultural pluralism.
By uncritically adopting Western cognitive metrics, it risks replicating the epistemic violence of historical psychometrics, where intelligence was weaponized to justify oppression under the guise of science. The absence of Indigenous, Global South, and marginalized voices in the narrative reflects a broader structural imbalance in AI governance, where power is concentrated in the hands of corporate and academic elites who benefit from opaque, proprietary systems. Cross-cultural perspectives reveal that intelligence is not a universal, decontextualized trait but a dynamic, relational process shaped by culture, history, and ecology—a reality the study's methodology fundamentally ignores. Moving forward, solution pathways must center decolonization, transparency, and historical accountability to ensure AI evaluation serves humanity rather than reinforcing existing hierarchies of knowledge and power.