Latin America Needs AI Benchmarks for Auditing and Optimization

A new paper highlights a significant gap in Latin America's AI infrastructure: the absence of a dedicated benchmark layer. This missing component prevents public institutions from independently auditing the performance and social alignment of AI systems developed elsewhere. Furthermore, local companies struggle to optimize AI solutions for specific regional challenges, leading to suboptimal performance. The authors argue that this deficit results in a loss of both auditability and strategic direction for AI development, which is becoming increasingly critical infrastructure. To address this, the paper introduces the concept of an EvalsHub, with LatamBoard proposed as its initial regional implementation. This open, task-first infrastructure would allow universities, public bodies, professional communities, and businesses to publish, execute, compare, and maintain AI evaluations. The goal is to create a persistent system where AI systems can be continuously measured and refined, fostering local AI innovation and ensuring accountability.