MERCI Cards: An LLM Evaluation and Deployment Framework for High-Stakes Domains

Abstract:As LLMs are increasingly deployed in high-stakes professional workflows, engineers and researchers require principled protocols to systematically track, monitor, and improve model performance across deployment cycles. We present a mathematical framework for iterative LLM evaluation and deployment, and demonstrate its application to AI systems used in criminal justice. Our framework formalizes LLM integration in high-stakes, high-risk, and resource-constrained domains across model selection, rubric design, evaluations and deployment via a weighted multi-objective optimization. We demonstrate that MERCI Cards can guide improvements of the system across deployment iterations, direct developer attention toward under-performing areas, and focus user attention on validation and error-correction in the LLM's outputs.

Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)

arXiv:2610.04430 [cs.AI]

(or arXiv:2610.04430v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.04430 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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