Epistemic Transfer in AI-Assisted Verification: A Framework and Evaluation Protocol
AI tools can improve claim judgments while leaving open what users can do later without them. This paper develops an evaluation framework for epistemic transfer: the effect of prior AI-assisted verification on delayed judgments of novel claims under a specified access regime. The contribution is a verification-specific synthesis of learning, transfer, and human--AI evaluation, organized around two complementary estimands. The Epistemic Transfer Effect (ETE) compares delayed performance after alternative practice conditions. Tool-Removal Cost (TRC) compares immediate performance with and without assistance after practice; despite its name, it measures a current availability effect, not ski...
| Authors | Christoph Trattner |
|---|---|
| Primary category | cs.HC |
| Published | 9 August 2026 |
| Last updated at source | 5 September 2026 |
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