Researchers have developed PSA-Eval, a novel framework for evaluating deployed AI agents in public spaces, focusing on identifying and rectifying failures rather than just overall scores. This approach extends the evaluation process to trace failures, enabling review, repair, and regression testing. A pilot study on a trilingual digital front-desk system revealed significant cross-language score drifts despite a high average score, indicating the framework's effectiveness in uncovering deployment issues. AI
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IMPACT Introduces a new methodology for evaluating deployed AI systems, potentially improving their reliability and safety in multilingual public-facing applications.
RANK_REASON Academic paper introducing a new evaluation framework for deployed AI agents.