AI RESEARCH
Making AI-Assisted Grant Evaluation Auditable without Exposing the Model
arXiv CS.LG
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ArXi:2604.25200v1 Announce Type: cross Public agencies are beginning to consider large language models (LLMs) as decision- tools for grant evaluation. This creates a practical governance problem: the model and scoring rubric should not be exposed in a way that allows applicants to optimize against them, yet the evaluation process must remain auditable, contestable, and accountable. We propose a TEE-based architecture that helps reconcile these requirements through remote attestation.