Making AI-Assisted Grant Evaluation Auditable without Exposing the Model
Public agencies are beginning to consider large language models (LLMs) as decision-support 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. The architecture allows an external verifier to check which model, rubric, prompt template, and input representation were used, without exposing model weights, proprietary scoring logic, or intermediate reasoning to applicants or infrastruc...
| Authors | Kemal Bicakci |
|---|---|
| Primary category | cs.CR |
| Published | 28 April 2026 |
| Last updated at source | 28 April 2026 |
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