🤖 AI Summary
This study addresses a critical gap in current AI evaluation methodologies, which often overlook the impact of low-resource deployment conditions—such as noisy inputs, limited hardware capabilities, and unstable network connectivity—on system usability. The work proposes a novel evaluation framework that treats the deployed system as the unit of assessment, integrating task performance with real-world deployment contexts across multiple dimensions. Departing from conventional leaderboard-based approaches, the framework tailors evaluation criteria to specific application categories and introduces a standardized reporting system comprising benchmark cards, deployment profiles, and failure-handling mechanisms. By balancing comparability with contextual sensitivity, this approach provides policymakers and practitioners with clear, actionable insights for informed AI deployment decisions.
📝 Abstract
Existing AI evaluation practices often fail to capture how systems actually perform in low-resource environments, where operational constraints shape usability as much as model quality. Through a structured analysis of existing benchmark families across speech, chat/RAG, and vision systems, we identify critical gaps between laboratory evaluation practices and real-world deployment conditions in low-resource environments. We argue that the meaningful unit of assessment is the deployed system rather than an isolated model and that effective evaluation frameworks must integrate task performance with deployment conditions such as noisy inputs, code-switching, intermittent connectivity, low-end hardware, and domain shift. At the same time, benchmarks should recognize that different application classes require distinct evaluation profiles rather than a single aggregate score that obscures operational differences. To support practical decision-making, we propose a shared reporting framework that preserves comparability across systems and application types while remaining sensitive to deployment context. Finally, we emphasize the need for concise and actionable reporting artifacts for policymakers, donors, and implementers, including standardized one-page benchmark cards, deployment profiles, and explicit documentation of failure handling procedures and human oversight mechanisms.