Verifiable evaluations of machine learning models using zkSNARKs

In a world of increasing closed-source commercial machine learning models, model evaluations from developers must be taken at face value. These benchmark results, whether over task accuracy, bias evaluations, or safety checks, are traditionally impossible to verify by a model end-user without the costly or impossible process of re-performing the benchmark on black-box model outputs. This work presents a method of verifiable model evaluation using model inference through zkSNARKs. The resulting zero-knowledge computational proofs of model outputs over datasets can …

accuracy benchmark bias checks commercial cs.ai cs.cr cs.lg developers face machine machine learning machine learning models process safety value verify world