3 papers
cs.CC2026
Interactive Proofs of Proximity for Model Evaluation
Geoffroy Couteau, Nikolas Melissaris, Tamara Paris
We study interactive proofs of proximity (IPPs) for model evaluation, where a resource-limited verifier interacts with an untrusted prover, typically the model owner, to certify st…
cs.CY2025
When Openness Fails: Lessons from System Safety for Assessing Openness in AI
Tamara Paris, Shalaleh Rismani
Most frameworks for assessing the openness of AI systems use narrow criteria such as availability of data, model, code, documentation, and licensing terms. However, to evaluate whe…
cs.AI2025
Opening the Scope of Openness in AI
Tamara Paris, AJung Moon, Jin Guo
The concept of openness in AI has so far been heavily inspired by the definition and community practice of open source software. This positions openness in AI as having positive co…